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- W2487463620 abstract "Human faces have always been an important entity in Biometric recognition systems as they are inherently associated with one's identity, gender, age group, ethnicity, etc. In recent years, there has been a growing interest in problems such as face recognition across ages, age estimation from images and videos, etc. which directly relates to modeling age related variations in facial appearance. Algorithms proposed in the past have mainly focused on images, but not video sequences. Also, past research works have focused on adult faces and young faces separately and hence an approach that is generalized for images from all possible age groups have not been explored. This dissertation addresses the problem of face recognition/verification across age progression by modeling the shape and textural changes of the face of a subject across age. In particular, this dissertation focuses on performing face verification and recognition using images and video sequences across age. For image based face recognition, a feature based approach in which facial features unique to a subject are extracted from the gallery of images is proposed. A graph based solution is proposed for spatial changes of the face, whereas a non-linear manifold based approach is proposed for face recognition in video sequences. The task of face verification in images is handled by an effective feature representation that captures the similarities and dissimilarities between the pair of age separated images. A pipeline approach that performs face categorization based on the discriminative cues thus reducing the search space for a face recognition system is proposed. Face categorization based on discriminative cues improves the performance of a face recognition/verification system in terms of accuracy, lower time requirements, and graceful degradation. The proposed pipeline system is adapted with findings from the human perception studies for more accurate face categorization of the images. Face recognition in video sequences are performed by mapping the high-dimensional data as data points in a low-dimensional manifold which can spatially represent the video sequences. The proposed algorithms are evaluated using various aging data sets containing unconstrained face images and videos. In particular, we have focused on videos taken using mobile phones." @default.
- W2487463620 created "2016-08-23" @default.
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- W2487463620 date "2012-01-01" @default.
- W2487463620 modified "2023-09-27" @default.
- W2487463620 title "Face recognition/verification across age progression in images and videos" @default.
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