Face Recognition Method Base On Quaternion Wavelet Magnitude/Phase Features

Face recognition is a typical image understanding and pattern classification problem, which has a great deal of potential applications in information security, criminal detection and access control. After the development of recent decades, face recognition technology has made considerable progress. At present the best face recognition system under controlled conditions have been able to achieve better recognition performance, however, evaluation results and practical experience have shown that: the face recognition technology is not yet mature under non-ideal conditions (illumination, expression and pose changes, et al.). Gabor wavelet transform has good spatial locality and orientation selectivity, which is a good face description method, so it is widely used in face recognition domain. Even though Gabor wavelet based face image representation is optimal in many respects, it is computationally very complex and memory requirements for storing Gabor features are very high. The main contribution of this paper is: comparing with the Gabor wavelet of traditional method, the quaternion wavelet magnitude phase features are used for face recognition and they have a significantly lower time complexity and are more conducive to practical application.Firstly, the construction and tight frame of quaternion wavelet transforms (QWT), real signal of quaternion wavelet transform and quaternion wavelet transform phase characteristics are studied, and then the quaternion wavelet transform magnitude phase representation is proposed. Combining the quaternion algebra and wavelet theory, this method is a near shift-invariant tight frame representation, whose coefficients support a magnitude and three phase, therefore it has robustness to expression and illumination changes. These quaternion magnitude and phases are combined and divided into several sub-blocks, and then each sub-block is classified by a nearest neighbor classifier. These sub-block classification results are voted to complete the ultimate face recognition.Comparing with the PCA method, the recognition rate of this method is respectively higher on the Indian, Yale, ORL, YaleB Extended face database and the CMU-PIE face database by 14.8%, 31.5%, 9.9%, 13% and 3.4%. The recognition rate of the proposed method is slightly higher than the LDA method on the ORL face database and outperforms the LDA method on the Indian, Yale, YaleB Extended face database and the CMU-PIE face database. The recognition rates of the four FERET'96 probe sets achieve 94.1%, 56.7%, 58.1% and 47.1%, respectively. Although EBGM has better recognition rate on the probe set Fc, our method is superior on other three probe sets. Moreover, the method has few free parameters to adjust and has much lower computation complexity. Any one of the FERET'96 face database images is selected, when the pixel of face image is 64×64, the running time of QWT feature extraction is only 0.047s, and Gabor wavelet transform reaches 0.566s. In addition, seven different illumination compensation methods are used for face image preprocessing on the YaleB Extended face database and FERET'96 face database. Combining the steerable filter and discrete cosine transform of normalization techniques, the recognition rate of this algorithm has improved obviously.The study shows that not only the proposed face recognition method can get a good robustness to the illumination, expression and gesture, but also the recognition performance has greatly improved comparing with traditional methods. This algorithm has potentials for various applications in image registration, face detection and facial feature location.