Abstract
The face recognition with expression and occlusion variation becomes the greatest challenge in biometric applications to recognize people. The proposed work concentrates on recognizing occlusion and seven kinds of expression variations such as neutral, surprise, happy, sad, fear, disgust and angry. During enrollment process, principle component analysis (PCA) detects facial regions on the input image. The detected facial region is converted into fuzzy domain data to make decision during recognition process. The Haar wavelet transform extracts features from the detected facial regions. The Nested Hidden markov model is employed to train these features and each feature of face image is considered as states in a Markov chain to perform learning among the features. The maximum likelihood for the input image was estimated by using Baum Welch algorithm and these features were kept on database. During recognition process, the expression and occlusion varied face image is taken as the test image and maximum likelihood for test image is found by following same procedure done in enrollment process. The matching score between maximum likelihood of input image and test image is computed and it is utilized by fuzzy rule based method to decide whether the test image belongs to authorized or unauthorized. The proposed work was tested among several expression varied and occluded face images of JAFFE and AR datasets respectively.
Highlights
The face recognition has rapidly emerged as an active research area in the biometric field to provide various secure real world applications like security monitoring, law enforcement and surveillance systems [1]
The template based methods [1] compare the given image with a set of stored templates that is generated by using statistical tools such as support vector machine (SVM), Linear discriminant analysis (LDA) and principle component analysis (PCA)
The proposed work uses Nested Hidden markov model (NHMM) with Baum Welch algorithm [16] to find the relevant characteristics of the image
Summary
The face recognition has rapidly emerged as an active research area in the biometric field to provide various secure real world applications like security monitoring, law enforcement and surveillance systems [1]. Most of real world applications using face recognition technology would require identifying a person under some controlled conditions like variations in illumination, pose, and expression [3]-[5] and occlusion [6]. The face is recognized under expression and occlusion variation with the highest recognition rate. Hidden Markov model (HMM) is one of statistical approach and it forms an observation vector sequence by considering every facial feature as a state in Markov chain. It calculates the similarity index with the training set to recognize faces. The proposed work uses Nested Hidden markov model (NHMM) with Baum Welch algorithm [16] to find the relevant characteristics of the image
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