Efficient Gabor Phase Based Illumination Invariant for Face Recognition

Abstract

This paper presents a novel Gabor phase based illumination invariant extraction method aiming at eliminating the effect of varying illumination on face recognition. Firstly, It normalizes varying illumination on face images, which can reduce the effect of varying illumination to some extent. Secondly, a set of 2D real Gabor wavelet with different directions is used for image transformation, and multiple Gabor coefficients are combined into one whole in considering spectrum and phase. Lastly, the illumination invariant is obtained by extracting the phase feature from the combined coefficients. Experimental results on the Yale B and the CMU PIE face database show that our method obtained a significant improvement over other related methods for face recognition under large illumination variation condition.

Multi-scale Local Difference Directional Number Pattern for Group-housed Pigs Recognition

“…It can be seen that the recognition rates of Luminance quantization were lower than that of MSB quantization by SVM with linear and RBF kernel function. That is because the MSB method produces values which better discriminate the colours [16,17]. Thus, it can better describe the colour information of different pigs.…”

“…However, pigs may move to different positions in a real pigsty with complex illumination. Therefore, Gabor phase response is also calculated in the proposed method because its insensitivity to illumination conditions [16,17]. The main difference of Gabor phase response on the vth scale is encoded by the same way, which is described as MLDDN_Pv(x, y).…”

“…Moreover, a real pigsty always under complex light conditions and pigs may move to different positions in the pigsty. Therefore, Gabor phase response which is insensitive to illumination conditions [16,17] is also used for feature extraction.…”

Illumination-Invariant Face Recognition in Hyperspectral Images

“…Kaur et al proposed a method that extracts LOG-DCT features from retinex images [11]. Fan et al proposed a method based on the phase of 2D Gabor features [12]. Zhu et al proposed using logarithm gradient orientation and logarithm gradient magnitude to derive gradient histogram [13].…”

“…For example, a variety of subspace-based methods have been proposed [3,4]. Methods based on illumination-invariant features have also been proposed [5][6][7][8][9][10][11][12][13][14]. On the other hand, previous studies have shown that spectral information is useful for this purpose [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”