International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 18 (2018) pp. 13460-13465 © Research India Publications. http://www.ripublication.com 13460 A Galois Field based Texture Representation for Face Recognition Shivashankar S. 1 , Medha Kudari 2 , Prakash S. Hiremath 3 1 Department of Computer Science, Karnatak University, Dharwad, Karnataka 580003, India. 2 Department of Computer Science, Karnatak University, Dharwad, Karnataka 580003, India. 3 Department of Computer Science (MCA), KLE Technological University, BVBCET, Hubballi, Karnataka 580029, India. Abstract This paper presents a Galois field based texture representation for face recognition. A Galois field has been used to represent texture in images. The facial images are divided into several local regions. Each of these local regions is represented using a novel Galois field based method. The bin values of the normalized cumulative histogram forms the feature vector for the region. These local features are concatenated to form the face descriptor. Extensive experiments are performed on FERET face database and extended Cohn Kanade face database. The results clearly show that the proposed method is better and effective as compared to Rotation Invariant Local Binary Pattern and Log-polar transform. Keywords: Galois Field, Texture, Face descriptor, Classification INTRODUCTION One of the properties that distinguish objects and images is texture, the other properties being shape and colour. Analysis of texture is one of the many aspects in image processing and computer vision. Texture analysis has been applied in the fields of visual inspection, remote sensing imagery, pattern recognition and image retrieval. Statistical [1], structural [2], model based [3] and signal processing models [4] are the common feature extraction methods. In [5], a survey of texture descriptors for texture classification is available. However, most of these approaches are sensitive to the changes of orientations and scales of the texture pattern. On the other hand, objects of interest under various orientations, scales, illumination and occlusion are often encountered in different applications, such as face recognition and signature verification. Therefore there arises a requirement to develop descriptors of texture which are insensitive or invariant to changes in rotation, scale, illumination and so on. There have been ongoing research activities to represent invariant texture. Many researchers have devoted their energies for representing texture in only rotation invariant environment. Some researchers have addressed the issue of scale invariance in images. Limited research is available on the area of rotation and scale invariant texture representation. The major existing approaches include psycho-physical transformation, multi- resolution simultaneous autoregressive (MRSAR) model [6], log-polar wavelet signatures [7], multichannel Gabor filtering [8] and the Wold model [9] for invariant texture analysis. Biometrics is the study of human biological measurements for identification and verification. Biological measurements like face, voice and fingerprints qualify as a biometric characteristic because it has the properties of universality, distinctiveness, permanence and collectability. In recent times, more biological measurements have been considered as a biometric like gait, signatures and iris [10]. Based on physiological characteristics, biometric traits include face [12], fingerprints [13], finger geometry, hand geometry [14], hand veins [15], palm, iris [16], retina [17], ear [18] and voice. Based on behavioural characteristics, biometric traits include gait [19], signature [20] and keystroke dynamics [21][11]. A biometric system is similar to a texture pattern recognition system where the biometric data is obtained from an individual, a feature set is extracted from the acquired data and then this acquired feature set is compared against the template set in the database. A biometric system may either verify or identify an individual based on the type of application the biometric system is being used. A biometric system can be used for either identification or verification purposes. In verification application, a user’s identity is validated by comparing the user’s captured biometric features against the user’s biometric features stored in the database. In identification application, a user is recognized by searching the templates of all the users in the database for a match [10]. Face recognition has emerged as a major ongoing research area in pattern recognition and computer vision. Face recognition is considered to be a difficult task than the usual pattern recognition problems due to the presence of few training samples (in some cases only one training sample) and numerous testing samples. The sources of variation in facial appearance can be categorized into two groups: intrinsic factors and extrinsic factors. Intrinsic factors are due to purely physical nature of the face and are independent of the observer. Extrinsic factors cause the appearance of the face to alter via the interaction of light with the face and observer like illumination, pose, scale and imaging parameters [11]. Current face recognition systems perform well under relatively controlled environments but tend to suffer when variations in different factors like rotation, scale, pose, illumination are present [24]. Thus finding good descriptors for the appearance of local facial regions is an open and ongoing issue. The researchers on texture analysis have developed a variety of different descriptors for the appearance of image patches. Heisele, Ho, Wu and Poggio developed a