A review of feature extraction techniques BTC, DCT, Walsh and PCA with FDM and BDM for face recognition

In the modern era the world comes nearer to every individual as an IT revolution where all the applications are computerized. As the level of security breaches and frauds of transaction increases, it requires highly secure identification and personal verification technologies. Though there are various biometric traits such as iris, fingerprint and palm print etc., we focused on face recognition as it is socially acceptable and reliable. Here user identity plays a very important role to uniquely verify or authenticate the individual person. Instead of designing more complex system, which is more expensive and which requires more software and hardware resources, it is essential to think about to bridge the gap which will create a system with simplicity, less costly and efficient, as well as socially acceptable. Utilizing biometrics for personal authentication is becoming convenient and considerably more accurate than current methods (such as the utilization of passwords or PINs). The factors which highly impact the face recognition system performance are illumination and pose variations. Feature extraction is the key to reach face recognition. In literature various feature extraction techniques in spatial and frequency domain are available. This paper gives overview of the existing feature extraction techniques PCA, DCT, Walsh and BTC for face recognition and comparative analysis.