Face recognition under varying illumination conditions using deep neural networks
P. Hema Sree, D. Nagajyothi · AIP conference proceedings · 2024
Face recognition is a biometric based application software capable of verifying or identifying exclusively a person by analyzing and comparing person's facial contours using patterns.The main applications of face recognition system is found in security systems, law enforcements and other initiatives.The variation in illumination, effects immensely on recognizing faces or images.Even for the familiar faces, illumination variations lead to alteration in the shadow shapes, shift in locat ions, fluctuations in features and contract gradient variations.Under these conditions, larger impact is observed to recognize these face images.A face recognition system is developed under these varying illumination conditions in this paper.Various methods of illumination normalization like Self-Quotient Image (SQI), Histogram equalization (HE), Gross & Barovick's anisotropic smoothing (GB), Gross & Barovick's anisotropic smoothing (GB) and Multiscale Retinex (MSR) are comparatively analyzed.Illumination invariant face recognition is developed by first acquiring 40 illumination variants face s.Face detection is performed by deformable template with scaled color map and its negative.Further face alignment is estimated by supervised learning by estimating the locations of a set of facial landmarks.Later image normalization is performed using Tan Trigg's Normalization Technique (TTVT) which includes Gamma correction, MaskingDifference of Gaussian (DOG) and contrast stretching methods [1].The resultant normalized face image is classified using Convolutional Neural Networks.The evaluation and performance analysis are discussed to determine the applicability and suitability for various applications.