Principal Gabor filters for face recognition

Abstract

Abstract-Gabor filters have proven themselves to be a powerful tool for facial feature extraction. An abundance of recognition techniques presented in the literature exploits these filters to achieve robust face recognition. However, while exhibiting desirable properties, such as orientational selectivity or spatial locality, Gabor filters have also some shortcomings which crucially affect the characteristics and size of the Gabor representation of a given face pattern. Amongst these shortcomings the fact that the filters are not orthogonal one to another and are, hence, correlated is probably the most important. This makes the information contained in the Gabor face representation redundant and also affects the size of the representation. To overcome this problem we propose in this paper to employ orthonormal linear combinations of the original Gabor filters rather than the filters themselves for deriving the Gabor face representation. The filters, named principal Gabor filters for the fact that they are computed by means of principal component analysis, are assessed in face recognition experiments performed on the XM2VTS and YaleB databases, where encouraging results are achieved.

Patch Based Collaborative Representation with Gabor Feature and Measurement Matrix for Face Recognition

“…Inspired by previous works, in this paper, we integrate Gabor feature into the PCRC framework to improve its robustness. A Gabor filter with multidirection and multiscale ] is defined as follows [25,26]:…”

“…, 4) is the amplitude of Gabor, ,] ( ) is the phase of Gabor, and the local energy change in the image is expressed by amplitude information. Because the Gabor phase changes periodically with the space position and the amplitude is relatively smooth and stable [19,25,26], only the magnitude of Gabor was used in this paper, such as Figure 2.…”

“…The Eigenface [20,21], Randomface [22], and Fisherface [21] are all classical holistic features [23], but some other works argued that those holistic features can be easily affected by variables such as illumination, pose, facial expression, and noise. Therefore, they introduced some local features such as LBP [24] and Gabor filter [25,26]. Gabor filter has been successfully and widely used in FR [26,27].…”

“…Therefore, they introduced some local features such as LBP [24] and Gabor filter [25,26]. Gabor filter has been successfully and widely used in FR [26,27]. Gabor feature could effectively extract the face local features at multiple scales and multiple directions; however, this may lead to a sharp rise in data volumes [28].…”

Face Recognition: A Survey

“…why an algorithm was proposed in which instead of using the Gabor filters alone, a combination of Gabor filters computed by using PCA. These filters were named Principal Gabor filters [85] and they facilitated in eliminating redundancy. These filters were able to identify the faces successfully.…”