Face Recognition Based On Local Texture Description

With the development of pattern recognition and computer vision,the application of face recognition is becoming increasingly important.Face recognition technology,because of its uniqueness and stability,has become rational and reliable method.It has become a sophisticated technology in the field of security,and is rapidly entering the market.The face recognition system usually consists of face detection,preprocessing,feature extraction,face recognition and matching.Feature extraction is the most important issue.In some specific circumstances,the identification rate of the extracted facial features is still difficult to achieve the desired requirements,especially influenced by changes of pose,illumination,noise and expression.Through the appropriate method,mapping the original face image to a feature space is an effective way to improve identification rate which can reduce the impact of these factors.The main research focuses on the face feature extraction,which can overcome the negative influences of noise,expression and illumination in face recognition system.The highlights of the main research are as follows:1.A novel face feature extraction method named local dual-cross ternary pattern(LDCTP)is proposed:Extracting effective features is a fundamental issue in image representation and recognition.However,it is difficult to achieve a reasonable tradeoff between discriminative power and robustness.LDCTP is a feature representation inspired by the sole textural structure of human faces.Under the same conditions,LDCTP has better recognition performance than other related feature descriptors,but it takes a little more time.2.A novel face feature extraction method named face recognition based on D-PSIFT local principal orientation histogram is proposed:Combining the advantages of SIFT and D-HLDO,presents an efficient and simple method for feature extraction of face images,which greatly reduces the computational complexity and makes the speed of calculation increased by nearly 3 times.3.The basic theory of feature integration and multiple kernel learning is introduced.Kernel methods have become popular in computer vision,particularly for visual object recognition.The key idea of kernel methods is to introduce nonlinearity into the decision function by mapping the original features to a higher dimensional space.The integration of LDCTP and D-PSIFT by the method of multiple kernel learning is proved to be effective.