International Journal of Latest Technology in Engineering, Management & Applied Science (IJLTEMAS) Volume VI, Issue I, January 2017 | ISSN 2278-2540 www.ijltemas.in Page 41 Partial Face Recognition Based on Gabor Texture Kernels Naveena M, G Hemantha Kumar Department of Studies in Computer Science, University of Mysore, Manasagangotri-570006, Mysore, India. Abstract: Biometrics refers to the recognition of individuals based on their physiological and behavioral traits. The Full and Partial face recognition is one of the challenging ongoing research works. For Public safety and national security enhance the needs for partial face recognition techniques, which are among the most secure and accurate authentication tools and these applications are not commonly used, yet, but the area is interesting especially in crime investigation. In this paper, we present the face recognition under partial visibility such of using partial face as biometric for person identification. The implemented method consists of three stages. In the first stage, pre-processing of both full and partial is done. In the second stage, features such as shape and textures are extracted. Finally, matching is done between pierced and non-pierced image of an individual. Keywords: Face recognition, feature vectors, Circle, Diagonal, Rectangle, Gabor Texture Kernels. I. INTRODUCTION ace biometric is considered as one of the most reliable and invariant biometrics characteristics in line with iris and fingerprint characteristics. Biometric systems have become very essential components in almost all security aspects. These systems perform the recognition of a human being based on physiological and behavioral characteristics. Physiological characteristics are related to the shape of the body. Biometric traits such as face, fingerprint, iris, hand geometry fall under this category. Behavioral characteristics are related to behavior of a person. Signature, voice, character strokes etc. are some of the biometric traits which fall under this category. Among the various physiological traits, face has gained much attention in recent years as it has been found to be a good and reliable biometrics for human verification and identification. Reason behind the partial face biometrics gaining popularity is that partial faces are remarkably consistent. Unlike faces, they do not change shape with different expressions or age, and remain fixed in the middle of the side of the head against a predictable background. To verify the various poses for a person identification we need to create a face database under the database need to perform certain operation and it can be comparing or testing with given the part of the a face image or partial image. All identification technologies operate using the following four stages: First stage is -Capture: A sample is captured by the camera during Enrolment and also in identification or verification process, it is taken by any digital camera and easy to use. Secondly, Feature Extraction: by this unique data is extracted from the sample by using different techniques and a template is created by using it on different platforms. Thirdly Comparison: the template is then compared with a sample. And then finally, match/non match: face recognition is very complex technology and is largely software based, the system decides if the features extracted from the new samples are a match or a non match. Edge detection: A photo of the subject‟s ear is taken and fed into the computer. The image undergoes through pre-processing steps. Then edge detection is carried out on this picture. From this detected edge shape of the ear, is separated. Next the features like pixels count, mean, standard deviation, and skewness are extracted from the face. Matching is being conducted between subject‟s non pierced face and pierced face. This match is compared with a predefined threshold value, which decides the identity of the person. It is used to locate areas with strong intensity contrast and helps in extracting information about an image. Canny edge detection is used in this system for edge detection. This step creates a fine image of ear using the edge value. II. MOTIVATION FOR THE PROPOSED SYSTEM The human face is a stable structure that does not change much in shape with the age and with facial expressions. Uniqueness of outer half shape that do not change because of emotion etc. partial is a workable new class of biometrics since the face has desirable properties such as universality, distinctiveness and stability, Although no one has proved that each person„s partial faces are unique and limited surface of the partial face allows faster processing compared with face. III. PROPOSED SYSTEM a. Texture based Face Detection and Verification: Recent research in texture-based ear recognition also indicates that face detection and texture-based partial face recognition are robust against signal degradation and encoding artifacts. Based on these findings, we further investigate and compare the performance of texture descriptors for face recognition under partial visible conditions and seek to explore possibilities to complement texture descriptors with depth information. On the basis of full face images from visible light F