Abstract
Face verification is a process to determine whether the two input faces are of the same person or not. Traditional face verification approaches have focused mainly on frontal faces, but for diverse applications, face verification should also work on faces across different pose variation. Therefore, this paper presents a method that is robust to pose changes from -90 to 90 degrees via ranked list of look-alikes. Proposed method works in two steps. First, we measure the similarity between the probe image and all the images in the library and get two ranked lists of look-alikes. Second, we measure the similarity between the two ranked lists, which is considered as the final similarity between the two images. We suggest a way to re-rank the look-alike lists emphasizing the duplicates in the list. Our experimental results on the CMU Multi-PIE database, which is one of the most extensive database in terms of pose variation, show improved performance over the other methods.
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Yun, S., Kim, J. (2015). Face Verification Across Pose via Look-Alike Ranked List Comparison. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_59
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DOI: https://doi.org/10.1007/978-3-319-16841-8_59
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
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