Multimodal biometric is an emerging area of research that aims at increasing the reliability of biometric systems through
utilizing more than one biometric in decision-making process. In this work, we develop a multi-algorithm based
multimodal biometric system utilizing face and ear features and rank and decision fusion approach. We use multilayer
perceptron network and fisherimage approaches for individual face and ear recognition. After face and ear recognition,
we integrate the results of the two face matchers using rank level fusion approach. We experiment with highest rank
method, Borda count method, logistic regression method and Markov chain method of rank level fusion approach. Due
to the better recognition performance we employ Markov chain approach to combine face decisions. Similarly, we get
combined ear decision. These two decisions are combined for final identification decision. We try with 'AND'/'OR'
rule, majority voting rule and weighted majority voting rule of decision fusion approach. From the experiment results,
we observed that weighted majority voting rule works better than any other decision fusion approaches and hence, we
incorporate this fusion approach for the final identification decision. The final results indicate that using multi algorithm
based can certainly improve the recognition performance of multibiometric systems.
Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to
everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific
expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video
frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are
computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the
results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.