Abstract
Occlusion detection in face verification is an essential problem that has not widely addressed. In this study the research is deals about occlusion detection in face recognition and estimation of human age using image processing. The objects hide from another object is called as occlusion. Occlusion conditions may vary from face wearing sunglasses, wearing of scarf in the eyes and mouth positions. The proposed work consists four stages. Initial stage is to extract the features using canny edge detection technique and to classify the occluded and non occluded region using Decision Tree Based Occlusion Detection (DTOD) classifier. Secondly the face verification and recognition is carried out using Elastic Matching Pattern (EMP) and Maximum Likelihood Classifier (MLC). Back Propagation Neural Network (BPNN) can be used to estimate the age of the human in the third stage. Our experiments are conducted on the database images for the first stage. By considering the first stage the various performance measures of the classifiers are analyzed. The correctly classified instances rate are high compared with the existing classifiers like random forest and bayes classifier. Experiments are conducted using ORL dataset for the second and the third stage. On the basis of the results obtained from the second stage we observed that the face verification was completed with 95% of accuracy. In the third stage, the age estimation using BPNN algorithm shows better performance results compared with the existing neural network algorithm.
Highlights
The face verification and recognition is carried out using Elastic Matching Pattern (EMP) and Maximum Likelihood Classifier (MLC)
On the basis of the results obtained from the second stage we observed that the face verification was completed with 95% of accuracy
Nowadays a major work from the research community is dedicated to occlusion detection in face verification and age estimation of a human
Summary
Nowadays a major work from the research community is dedicated to occlusion detection in face verification and age estimation of a human. Human face is robust as it changes within a short span of time. The Morphological approach, the idea and technique for the analysis and processing of geometrical structures based on set theory and random functions. The goal of morphological processing is to eliminate the imperfections added during segmentation. The appearance based approach, the technique for analysis and processing based on the features of the face which can be covering mask or sunglasses. The tree formation for occluded and non occluded region of the face using decision tree induction approach. The decision tree C4.3 with the AdaBoost techniques are combined to produce the decision Tree C5.0 algorithm. Ichikawa et al (2008) have implemented the usage of decision tree C4.3 algorithm, to classify the occluded and non occluded part of the facial image. The decision tree C4.3 with the AdaBoost techniques are combined to produce the decision Tree C5.0 algorithm. Ichikawa et al (2008) have implemented the usage of decision tree C4.3 algorithm, to classify the occluded and non occluded part of the facial image. Ling et al (2010) have discussed a discriminative approach to fully capture the face features, Support Vector Machine (SVM) and Gradient Orientation Pyramid (GOP) to be applied for face
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