Face Recognition from Images with High Pose Variations by Transform Vector Quantization

Pose and illumination variations are the most dominating and persistent challenges haunting face recognition, leading to various highlycomplex 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.