An Efficient Hybrid Face Recognition Algorithm Using PCA and GABOR Wavelets

Abstract

With the rapid development of computers and the increasing, mass use of high-tech mobile devices, vision-based face recognition has advanced significantly. However, it is hard to conclude that the performance of computers surpasses that of humans, as humans have generally exhibited better performance in challenging situations involving occlusion or variations. Motivated by the recognition method of humans who utilize both holistic and local features, we present a computationally efficient hybrid face recognition method that employs dual-stage holistic and local feature-based recognition algorithms. In the first coarse recognition stage, the proposed algorithm utilizes Principal Component Analysis (PCA) to identify a test image. The recognition ends at this stage if the confidence level of the result turns out to be reliable. Otherwise, the algorithm uses this result for filtering out top candidate images with a high degree of similarity, and passes them to the next fine recognition stage where Gabor filters are employed. As is well known, recognizing a face image with Gabor filters is a computationally heavy task. The contribution of our work is in proposing a flexible dual-stage algorithm that enables fast, hybrid face recognition. Experimental tests were performed with the Extended Yale Face Database B to verify the effectiveness and validity of the research, and we obtained better recognition results under illumination variations not only in terms of computation time but also in terms of the recognition rate in comparison to PCA- and Gabor wavelet-based recognition algorithms.

Two-Factor Authentication Methodology using Hybrid Face Recognition

“…After Histogram Equalization is performed, it utilizes a Garbor Wavelet and a MLBP to obtain higher recognition rates. At the end, the classifier reduces the number of candidate training images by selecting only the upper n training images after sorting them in ascending order according to their distance values between a test image and training images [12]. The main advantage of this coarse-to-fine step is reducing of the recognition time and a flexible two-step recognition structure.…”

A Survey of 2D Face Recognition Techniques

“…• PCA and Gabor wavelets [85]: This is a new approach that uses a face recognition algorithm with two steps of recognition based on both global and local features. For the first step of the coarse recognition, the proposed algorithm applies the principal components analysis (PCA) to identify a test image.…”

Face Recognition: A Survey

“…Gabor Filters were presented for 1-D Signals by Dennis Gabor, Later Daugman rediscovered and generalized them to 2-D Gabor Filters [68]. Gabor wavelet [69] method is such a method that uses local features for face recognition. Multi-Orientational information of a face image can be extracted by using the Gabor Wavelets.…”