On Detectors and Descriptors based Techniques for Face Recognition

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Out of all forms of biometrics, Face Recognition (FR) emerges as the most incredible one. Apart from offering revolutionary applications for business and law-enforcement purposes, it has also opened numerous research avenues in various domains like security, surveillance and social network. One of the many factors critical to having an efficient face recognition system is having at hand, a suitable combination of feature descriptor and feature detector. A feature detector makes use of methods that make local decisions regarding the presence/absence of image features of a given type. A feature descriptor, on the other hand, simplifies the image by extracting useful information and disposing irrelevant information. Our research discusses the goodness of various feature descriptor-detector combination. We do this by simply carrying out the process of feature matching using various combinations of detectors and descriptors. This experiment includes incorporation of dimensionality reduction on the images using Hypercomplex Fourier Transform (HFT) and RANSAC for noise reduction. Out of the diverse options available, we chose to test LGHD, PCEHD and EHD for feature descriptors; for feature detectors, we chose the ones that make use of popular algorithms like Harris-Stephen Algorithm, Minimum Eigen Value and SURF. A series of strict and thorough experiments on popularly available datasets - Faces94 and Grimace led us to an astonishing observation - an accuracy of 90.67% for the former and 71.3% for the latter for Minimum Eigen Value paired with LGHD! This in comparison to those for all other combinations is a lot superior. We thereby conclusively state that feature detector using Minimum Eigen Value algorithm paired with feature descriptor - LGHD outplays other combinations making this combination the best choice for Face Recognition.