Face Recognition has been a highly active research area for many years for its wide potential applications, such as law enforcement, security access, and man-machine interaction. The existing techniques have reached maturity in their capability but cannot still fully address practical challenges including the problem of recognition under varying facial expressions. Another commercially important challenge is handling high dimensionality data for large face databases, i.e., dimensionality reduction without much compromise on accuracy of recognition.
Researchers at Arizona State University have developed a novel technique that can achieve super-compact, space efficient, highly compressed, intelligent feature extraction with varying expressions, that enables to compactly pack the information pertaining to facial expressions (from many images of an subject) into only two feature images, with negligible information loss for recognition. This includes critically down-sampled face images or its ultra-low dimensional random projection which can contain as low as, say just 25 data points, for each subject in a fairly large face database (with thousands of images and hundreds of subjects). Typically the existing techniques need far higher dimensions, at the least of hundreds of data points to achieve good classification results.
- Face Recognition systems
- Compact imaging solutions
- Digital storage media
Benefits and Advantages
- Super compact, space efficient, and highly compressed face-representation and feature extraction
- Ultra-low operating dimensionality for recognition (at least 4 times less than existing techniques)
- Integration with Compressed Sensing architecture: Compressed Sensing technique is a recent innovation which offers many advantages over conventional imaging.
- Object recognition, ensemble data compression
- Down sampled up to just 25 data points per person, compared to at least 100s of data points with existing techniques.