Abstract
Pose and illumination variations are the most dominating and persistent challenges haunting face recognition, leading to various highly-complex 2D and 3D model based solutions. We present a novel transform vector quantization (TVQ) method which is fast and accurate and yet significantly less complex than conventional methods. TVQ offers a flexible and customizable way to capture the pose variations. Use of transform such as DCT helps compressing the image data to a small feature vector and judicious use of vector quantization helps to capture the various poses into compact codebooks. A confidence measure based sequence analysis allows the proposed TVQ method to accurately recognize a person in only 3-9 frames (less than 1/2 a second) from a video sequence of images with wide pose variations.
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© 2006 Springer-Verlag Berlin Heidelberg
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Das, A., Balwani, M., Thota, R., Ghosh, P. (2006). Face Recognition from Images with High Pose Variations by Transform Vector Quantization. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_60
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DOI: https://doi.org/10.1007/11949619_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
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