Statins, inhibitors of cholesterol synthesis for treating dyslipidemia and preventing cardiovascu... more Statins, inhibitors of cholesterol synthesis for treating dyslipidemia and preventing cardiovascular complications, have been shown to alter central nervous system functions. Our aim was to investigate the effects of the fluvastatin, a member of statin family, on psychomotor performance, daily activity and spatial memory. Sprague-Dawley rats were treated with fluvastatin (n = 8) or placebo as a control (n = 11) regardless of sex. Fluvastatin (7.5 mg/kg) was administered orally once a day for four weeks, while the control group was administered only placebo. Psychomotor performance was measured by rotarod tests. No significant difference was observed in the fluvastatin group over the course of weeks, but the control group preferred to stay on the device shorter times (p < 0.05). For the first three weeks of the drug administration there was a statistical difference between the groups, however no difference was found after the 4th week. There was no difference in the Barnes maze spatial memory test between the groups and also within the groups over the course of time. Daily activity tests revealed that stereotypical and vertical movements of the fluvastatin group were significantly less than the control group in all four weeks. Ambulatory movements and the distances taken by the fluvastatin group were decreased significantly over the course of time (p < 0.005 and p < 0.001, respectively), but the control group did not reveal any significant change. Our results suggest that fluvastatin altered psychomotor performance and daily activity in rats, but it did not affect the spatial memory. These behavioral changes might be associated with alterations in the composition of the brain lipids caused by fluvastatin.
This paper presents a real-time background estimation and maintenance based people tracking techn... more This paper presents a real-time background estimation and maintenance based people tracking technique in an indoor and an outdoor environments for visual surveillance system. In order to detect foreground objects, first, background scene model is statistically learned using the redundancy of the pixel intensity values during learning stage, even the background is not completely stationary. A background maintenance model is also proposed for preventing some kind of falsies, such as, illumination changes, or physical changes. And then for people detection, candidate foreground regions are detected using thresholding, noise cleaning and their boundaries extracted using morphological filters. From these, a body posture is estimated depending on skeleton of the regions. Finally, the trajectory of the people in motion is implemented for analyzing the people actions tracked in the video sequences. Experimental results demonstrate robustness and real-time performance of the algorithm.
Human identification at distance by analysis of gait patterns extracted from video has recently b... more Human identification at distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Based on normalized correlation on the distance vectors, gait cycle estimation is also performed to extract the gait cycle. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles.
Statins, inhibitors of cholesterol synthesis, are used to prevent cardiovascular complications. M... more Statins, inhibitors of cholesterol synthesis, are used to prevent cardiovascular complications. Moreover, statins have been shown to influence some cognitive functions. The modulating effects of simvastatin, one member of the statin family, on memory-related neurotransmitters and neuronal structures have also been reported. We aimed to investigate the behavioral effects of long-term simvastatin application on daily activity, psychomotor performance and spatial memory using Sprague-Dawley rats. Simvastatin (10 or 30 mg/kg/day) was administered orally to rats, in parallel with a vehicle-treated group. Daily activity test results of both simvastatin groups were found similar to the vehicle group after five weeks of simvastatin or vehicle application. Psychomotor performance was measured with the rotarod test. After 6 weeks of simvastatin or vehicle application, the vehicle-treated group stayed on the rotarod device for a shorter time compared with both simvastatin-treated groups. Spatial memory was evaluated by the Barnes maze test. Four weeks of 10 mg/kg/day simvastatin application led to poorer scores on spatial memory compared to the vehicle group, but surprisingly, this effect was not seen in the 30 mg/kg/day group. Our results revealed that simvastatin administration had no significant effect on daily activity. Psychomotor performance test results suggested that simvastatin alters psychomotor behavior at higher nervous system levels. Spatial memory test results indicate that long-term simvastatin usage impairs spatial memory only at 10 mg/kg/day dose.
In this study, motion detection and tracking for real time system is presented. Through the low p... more In this study, motion detection and tracking for real time system is presented. Through the low pass operations, the system works efficiently in real time. In the study, the Barnes Maze test mechanism is automatically learned by Hough transform. Background subtraction algorithms for object detection and estimation approaches based on color, shape and position for tracking are used. Since the desired results are related to the object organs, silhouette analysis is also used. The system observes the experiment mechanism. To detect the target (e.g. cheese), rat motions in the platform are tracked using camera vision system and then the motion positions in 2-dimensional are recorded. These data can be evaluated physically and psychologically. In this study making the learning model of the object from its behaviours is also the future work. For this purpose Markov processes could be used
This paper presents a approach for gait recognition based on binarized silhouette of a motion obj... more This paper presents a approach for gait recognition based on binarized silhouette of a motion object which is represented by distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. First, gait cycle estimation is performed based on normalized correlation on the distance vectors. Gait patterns are then extracted by using distance vectors for each projection independently. Then gait patterns are normalized according to dimensions of bounding box and gait cycle. Second, PCA based eigenspace transform is applied to gait patterns and Euclidean distances based supervised pattern classification is finally performed in the lower-dimensional eigenspace for human identification. Experimental results on four databases (CMU MoBo, SOTON, USF, NLPR) show that the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
In this work, a novel gait pattern production approach is proposed for human gait recognition. Ga... more In this work, a novel gait pattern production approach is proposed for human gait recognition. Gait biometric features are determined based on silhouttes. Proposed gait pattern is obtained by sum of the different silhouttes over a gait cycle. Then transformation matrix is produced with principal component analysis of training data set obtained from gait patterns. Each pattern is transformed into another domain and classified here. Classification is done with nearest neighbor algorithm. Successful results are achieved with the proposed method even when silhouttes can't be acquired well.
This paper presents a novel method for rat detection and tracking in a platform known as elevated... more This paper presents a novel method for rat detection and tracking in a platform known as elevated-plus maze and for recording the rat movements as a time elapsed in specific regions in real time surveillance systems. First, the location of plus maze platform is automatically determined by using hough transform. Rats in the platform are detected by applying otsu based
Introduction: The palmprint is a relatively new biometric feature, and can be used to recognise a... more Introduction: The palmprint is a relatively new biometric feature, and can be used to recognise a person based on unique features in their palm, such as the principal lines, wrinkles, ridges, minutiae points, singular points, texture etc. Researchers have recently developed ...
This paper presents a new approach for human identification at a distance using gait recognition.... more This paper presents a new approach for human identification at a distance using gait recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four view directions to silhouette. Based on normalized correlation on the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, eigenspace transformation based on PCA is applied to time-varying distance vectors and then Mahalanobis and normalized Euclidean distances based supervised pattern classification is finally performed in the lower-dimensional eigenspace for human identification. Experimental results on two main database demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the walking styles for data set of 25 people, and other data set of 22 people.
This paper presents a new novel approach is described for real- time human/vehicle classification... more This paper presents a new novel approach is described for real- time human/vehicle classification and motion analysis in real visual surveillance scene. Spatio-temporal 1-D signals based on the distances between the outer contour of binarized silhouette of a motion object and a bounding box placed around the silhou- ette are chosen as the basic image features called the distance vectors. The spatio-temporal distance vectors are extracted us- ing four view directions to the outer of the silhouette from the bounding box, they are top-, bottom-, left-, and right-views. De- pending on the distance vectors, correlation-based a similarity function in the time domain is produced for each view direc- tions to classify the motion objects and a similarity function in the frequency domain is then also extracted to analysis hu- man motions. Experimental results on the different test image sequences demonstrate that the proposed algorithm has an en- couraging performance with relatively robust and low computa- tional cost.
This paper presents a novel view independent approach on silhouette based human motion analysis f... more This paper presents a novel view independent approach on silhouette based human motion analysis for gait recognition applications. Spatio-temporal 1-D signals based on the differences between the outer of binarized silhouette of a motion object and a bounding box placed around silhouette are chosen as the basic image features called the distance vectors. The distance vectors are extracted using four view directions to silhouette. Gait cycle estimation and motion analysis are then performed by using normalized correlation on the distance vectors. Initial experiments for human identification are finally presented. Experimental results on the different test image sequences demonstrate that the proposed algorithm has an encouraging performance with relatively robust, low computational cost, and recognition rate for gait-based human identification.
Recognizing people by gait has a unique advantage over other biometrics: it has potential for use... more Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. In this paper, an improved method for gait recognition is proposed. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (KPCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Classic linear feature extraction approaches, such as PCA, LDA, and FLDA, only take the 2-order statistics among gait patterns into account, and are not sensitive to higher order statistics of data. Therefore, KPCA is used to extract higher order relations among gait patterns for future recognition. Fast Fourier Transform (FFT) is employed as a preprocessing step to achieve translation invariant on the gait patterns accumulated from silhouette sequences which are extracted from the subjects walk in different speed and/or different time. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.
This paper presents a wavelet based kernel principal component analysis (KPCA) palmprint recognit... more This paper presents a wavelet based kernel principal component analysis (KPCA) palmprint recognition method for human identification. The intensity values of palmprint images are first normalized by using their mean and their standard deviation. The normalized images are then transformed to the spectral domain by using wavelet transform and lowest frequencies are selected by filtering. Next, the feature vectors are formed with KPCA method which divergences samples on the nonlinear space. Finally, weighted Euclidean distance based nearest neighbor method is realized for palmprint classification. Experiments are performed on the most-well known public palmprint database, PolyU, includes 600 samples of 100 different persons.
In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) re... more In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) representation of palm images by two dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalized classification method based on Kernel Principal Component Analysis (Kernel PCA). Wavelet subband coefficients can effectively capture substantial palm features while keeping computational complexity low. We then kernel transforms to each possible training palm samples and then mapped the high-dimensional feature space back to input space. Weighted Euclidean linear distance based nearest neighbor classifier is finally employed for recognition. We carried out extensive experiments on PolyU Palmprint database includes 7752 palms from 386 different palms. Detailed comparisons with earlier published results are provided and our proposed method offers better recognition accuracy (99.654%).
Recognition of a person from gait is a biometric of increasing interest. This paper presents a ne... more Recognition of a person from gait is a biometric of increasing interest. This paper presents a new approach on silhouette representation to extract gait patterns for human recognition. Silhouette shape of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette. Second, eigenspace transformation based on Principal Component Analysis is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for recognition. A fusion task is finally executed to produce final decision. Experimental results on three databases show that the proposed method is an effective and efficient gait representation for human identification, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
Statins, inhibitors of cholesterol synthesis for treating dyslipidemia and preventing cardiovascu... more Statins, inhibitors of cholesterol synthesis for treating dyslipidemia and preventing cardiovascular complications, have been shown to alter central nervous system functions. Our aim was to investigate the effects of the fluvastatin, a member of statin family, on psychomotor performance, daily activity and spatial memory. Sprague-Dawley rats were treated with fluvastatin (n = 8) or placebo as a control (n = 11) regardless of sex. Fluvastatin (7.5 mg/kg) was administered orally once a day for four weeks, while the control group was administered only placebo. Psychomotor performance was measured by rotarod tests. No significant difference was observed in the fluvastatin group over the course of weeks, but the control group preferred to stay on the device shorter times (p < 0.05). For the first three weeks of the drug administration there was a statistical difference between the groups, however no difference was found after the 4th week. There was no difference in the Barnes maze spatial memory test between the groups and also within the groups over the course of time. Daily activity tests revealed that stereotypical and vertical movements of the fluvastatin group were significantly less than the control group in all four weeks. Ambulatory movements and the distances taken by the fluvastatin group were decreased significantly over the course of time (p < 0.005 and p < 0.001, respectively), but the control group did not reveal any significant change. Our results suggest that fluvastatin altered psychomotor performance and daily activity in rats, but it did not affect the spatial memory. These behavioral changes might be associated with alterations in the composition of the brain lipids caused by fluvastatin.
This paper presents a real-time background estimation and maintenance based people tracking techn... more This paper presents a real-time background estimation and maintenance based people tracking technique in an indoor and an outdoor environments for visual surveillance system. In order to detect foreground objects, first, background scene model is statistically learned using the redundancy of the pixel intensity values during learning stage, even the background is not completely stationary. A background maintenance model is also proposed for preventing some kind of falsies, such as, illumination changes, or physical changes. And then for people detection, candidate foreground regions are detected using thresholding, noise cleaning and their boundaries extracted using morphological filters. From these, a body posture is estimated depending on skeleton of the regions. Finally, the trajectory of the people in motion is implemented for analyzing the people actions tracked in the video sequences. Experimental results demonstrate robustness and real-time performance of the algorithm.
Human identification at distance by analysis of gait patterns extracted from video has recently b... more Human identification at distance by analysis of gait patterns extracted from video has recently become very popular research in biometrics. This paper presents multi-projections based approach to extract gait patterns for human recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Eigenspace transformation is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for human identification. A fusion strategy developed is finally executed to produce final decision. Based on normalized correlation on the distance vectors, gait cycle estimation is also performed to extract the gait cycle. Experimental results on four databases demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the same walking styles, and in top three-four matches 100% of the times for training and testing sets corresponds to the different walking styles.
Statins, inhibitors of cholesterol synthesis, are used to prevent cardiovascular complications. M... more Statins, inhibitors of cholesterol synthesis, are used to prevent cardiovascular complications. Moreover, statins have been shown to influence some cognitive functions. The modulating effects of simvastatin, one member of the statin family, on memory-related neurotransmitters and neuronal structures have also been reported. We aimed to investigate the behavioral effects of long-term simvastatin application on daily activity, psychomotor performance and spatial memory using Sprague-Dawley rats. Simvastatin (10 or 30 mg/kg/day) was administered orally to rats, in parallel with a vehicle-treated group. Daily activity test results of both simvastatin groups were found similar to the vehicle group after five weeks of simvastatin or vehicle application. Psychomotor performance was measured with the rotarod test. After 6 weeks of simvastatin or vehicle application, the vehicle-treated group stayed on the rotarod device for a shorter time compared with both simvastatin-treated groups. Spatial memory was evaluated by the Barnes maze test. Four weeks of 10 mg/kg/day simvastatin application led to poorer scores on spatial memory compared to the vehicle group, but surprisingly, this effect was not seen in the 30 mg/kg/day group. Our results revealed that simvastatin administration had no significant effect on daily activity. Psychomotor performance test results suggested that simvastatin alters psychomotor behavior at higher nervous system levels. Spatial memory test results indicate that long-term simvastatin usage impairs spatial memory only at 10 mg/kg/day dose.
In this study, motion detection and tracking for real time system is presented. Through the low p... more In this study, motion detection and tracking for real time system is presented. Through the low pass operations, the system works efficiently in real time. In the study, the Barnes Maze test mechanism is automatically learned by Hough transform. Background subtraction algorithms for object detection and estimation approaches based on color, shape and position for tracking are used. Since the desired results are related to the object organs, silhouette analysis is also used. The system observes the experiment mechanism. To detect the target (e.g. cheese), rat motions in the platform are tracked using camera vision system and then the motion positions in 2-dimensional are recorded. These data can be evaluated physically and psychologically. In this study making the learning model of the object from its behaviours is also the future work. For this purpose Markov processes could be used
This paper presents a approach for gait recognition based on binarized silhouette of a motion obj... more This paper presents a approach for gait recognition based on binarized silhouette of a motion object which is represented by distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. First, gait cycle estimation is performed based on normalized correlation on the distance vectors. Gait patterns are then extracted by using distance vectors for each projection independently. Then gait patterns are normalized according to dimensions of bounding box and gait cycle. Second, PCA based eigenspace transform is applied to gait patterns and Euclidean distances based supervised pattern classification is finally performed in the lower-dimensional eigenspace for human identification. Experimental results on four databases (CMU MoBo, SOTON, USF, NLPR) show that the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
In this work, a novel gait pattern production approach is proposed for human gait recognition. Ga... more In this work, a novel gait pattern production approach is proposed for human gait recognition. Gait biometric features are determined based on silhouttes. Proposed gait pattern is obtained by sum of the different silhouttes over a gait cycle. Then transformation matrix is produced with principal component analysis of training data set obtained from gait patterns. Each pattern is transformed into another domain and classified here. Classification is done with nearest neighbor algorithm. Successful results are achieved with the proposed method even when silhouttes can't be acquired well.
This paper presents a novel method for rat detection and tracking in a platform known as elevated... more This paper presents a novel method for rat detection and tracking in a platform known as elevated-plus maze and for recording the rat movements as a time elapsed in specific regions in real time surveillance systems. First, the location of plus maze platform is automatically determined by using hough transform. Rats in the platform are detected by applying otsu based
Introduction: The palmprint is a relatively new biometric feature, and can be used to recognise a... more Introduction: The palmprint is a relatively new biometric feature, and can be used to recognise a person based on unique features in their palm, such as the principal lines, wrinkles, ridges, minutiae points, singular points, texture etc. Researchers have recently developed ...
This paper presents a new approach for human identification at a distance using gait recognition.... more This paper presents a new approach for human identification at a distance using gait recognition. Binarized silhouette of a motion object is represented by 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four view directions to silhouette. Based on normalized correlation on the distance vectors, gait cycle estimation is first performed to extract the gait cycle. Second, eigenspace transformation based on PCA is applied to time-varying distance vectors and then Mahalanobis and normalized Euclidean distances based supervised pattern classification is finally performed in the lower-dimensional eigenspace for human identification. Experimental results on two main database demonstrate that the right person in top two matches 100% of the times for the cases where training and testing sets corresponds to the walking styles for data set of 25 people, and other data set of 22 people.
This paper presents a new novel approach is described for real- time human/vehicle classification... more This paper presents a new novel approach is described for real- time human/vehicle classification and motion analysis in real visual surveillance scene. Spatio-temporal 1-D signals based on the distances between the outer contour of binarized silhouette of a motion object and a bounding box placed around the silhou- ette are chosen as the basic image features called the distance vectors. The spatio-temporal distance vectors are extracted us- ing four view directions to the outer of the silhouette from the bounding box, they are top-, bottom-, left-, and right-views. De- pending on the distance vectors, correlation-based a similarity function in the time domain is produced for each view direc- tions to classify the motion objects and a similarity function in the frequency domain is then also extracted to analysis hu- man motions. Experimental results on the different test image sequences demonstrate that the proposed algorithm has an en- couraging performance with relatively robust and low computa- tional cost.
This paper presents a novel view independent approach on silhouette based human motion analysis f... more This paper presents a novel view independent approach on silhouette based human motion analysis for gait recognition applications. Spatio-temporal 1-D signals based on the differences between the outer of binarized silhouette of a motion object and a bounding box placed around silhouette are chosen as the basic image features called the distance vectors. The distance vectors are extracted using four view directions to silhouette. Gait cycle estimation and motion analysis are then performed by using normalized correlation on the distance vectors. Initial experiments for human identification are finally presented. Experimental results on the different test image sequences demonstrate that the proposed algorithm has an encouraging performance with relatively robust, low computational cost, and recognition rate for gait-based human identification.
Recognizing people by gait has a unique advantage over other biometrics: it has potential for use... more Recognizing people by gait has a unique advantage over other biometrics: it has potential for use at a distance when other biometrics might be at too low a resolution, or might be obscured. In this paper, an improved method for gait recognition is proposed. The proposed work introduces a nonlinear machine learning method, kernel Principal Component Analysis (KPCA), to extract gait features from silhouettes for individual recognition. Binarized silhouette of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Classic linear feature extraction approaches, such as PCA, LDA, and FLDA, only take the 2-order statistics among gait patterns into account, and are not sensitive to higher order statistics of data. Therefore, KPCA is used to extract higher order relations among gait patterns for future recognition. Fast Fourier Transform (FFT) is employed as a preprocessing step to achieve translation invariant on the gait patterns accumulated from silhouette sequences which are extracted from the subjects walk in different speed and/or different time. The experiments are carried out on the CMU and the USF gait databases and presented based on the different training gait cycles. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.
This paper presents a wavelet based kernel principal component analysis (KPCA) palmprint recognit... more This paper presents a wavelet based kernel principal component analysis (KPCA) palmprint recognition method for human identification. The intensity values of palmprint images are first normalized by using their mean and their standard deviation. The normalized images are then transformed to the spectral domain by using wavelet transform and lowest frequencies are selected by filtering. Next, the feature vectors are formed with KPCA method which divergences samples on the nonlinear space. Finally, weighted Euclidean distance based nearest neighbor method is realized for palmprint classification. Experiments are performed on the most-well known public palmprint database, PolyU, includes 600 samples of 100 different persons.
In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) re... more In this paper, we propose an efficient palmprint recognition scheme which has two features: 1) representation of palm images by two dimensional (2-D) wavelet subband coefficients and 2) recognition by a modular, personalized classification method based on Kernel Principal Component Analysis (Kernel PCA). Wavelet subband coefficients can effectively capture substantial palm features while keeping computational complexity low. We then kernel transforms to each possible training palm samples and then mapped the high-dimensional feature space back to input space. Weighted Euclidean linear distance based nearest neighbor classifier is finally employed for recognition. We carried out extensive experiments on PolyU Palmprint database includes 7752 palms from 386 different palms. Detailed comparisons with earlier published results are provided and our proposed method offers better recognition accuracy (99.654%).
Recognition of a person from gait is a biometric of increasing interest. This paper presents a ne... more Recognition of a person from gait is a biometric of increasing interest. This paper presents a new approach on silhouette representation to extract gait patterns for human recognition. Silhouette shape of a motion object is first represented by four 1-D signals which are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette. Second, eigenspace transformation based on Principal Component Analysis is applied to time-varying distance vectors and the statistical distance based supervised pattern classification is then performed in the lower-dimensional eigenspace for recognition. A fusion task is finally executed to produce final decision. Experimental results on three databases show that the proposed method is an effective and efficient gait representation for human identification, and the proposed approach achieves highly competitive performance with respect to the published gait recognition approaches.
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