Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features... more
Face recognition has become one of the most active research areas of pattern recognition since the early 1990s. This project thesis proposes a novel face recognition method based on Simplified Fuzzy ARTMAP (SFAM). For extracting features to be used for classification, combination of Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) is used. This is for improving the capability of LDA and PCA when used alone.PCA reduces the dimensionality of input face images while LDA extracts the features that help the classifier to classify the input face images. The classifier employed was SFAM. Experiment is conducted on ORL, Yale and Indian Face Database and results demonstrate SFAM’s efficiency as a recognizer. The training time of SFAM is negligible. SFAM has the added advantage that the network is adaptive, that is, during testing phase if the network comes across a new face that it is not trained for; the network identifies this to be a new face and also learns this new face. Thus SFAM can be used in applications where database needs to be updated frequently. SFAM thus proves itself to be an efficient recognizer when a speedy, accurate and adaptive Face Recognition System is required.
In this paper, we present recognition of handwritten characters of Arabic script. Arabic is now the 6th most spoken language in the world and is spoken by more than 200 million people worldwide. The 7th Century A.D., Arabic started to... more
In this paper, we present recognition of handwritten characters of Arabic script. Arabic is now the 6th most spoken language in the world and is spoken by more than 200 million people worldwide. The 7th Century A.D., Arabic started to spread to the Middle East as many people started to convert to Islam. During this time of religious conversions, Arabic replaced many South Arabian languages, most of which are no longer commonly spoken or understood languages. The challenges in Arabic handwritten character recognition wholly lie in the variation and disfigurement of Arabic handwritten characters, since different Arabic people may use a different style of handwriting, and direction to draw the same shape of the characters of their known Arabic script. Though various new propensity and technologies come out in these days, still handwriting is playing an important role. To recognize Arabic handwritten data there are different strategies like Simplified Fuzzy ARTMAP and Hidden Markov Models (HMM). In this paper, we are using Simplified Fuzzy ARTMAP, which is an updated version of Predictive Adaptive Resonance Theory. It also has a capacity to adjust clusters, as per the requirements Arabic script, which is remunerative to mitigate noise. We have tested our method on Arabic scripts and we have obtained encouraging results from our proposed technique.
