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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Face Recognition from Images with High Pose Variations by Transform Vector Quantization
verfasst von : Amitava Das, Manoj Balwani, Rahul Thota, Prasanta Ghosh
Erschienen in: Computer Vision, Graphics and Image Processing
Verlag: Springer Berlin Heidelberg
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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.