Abstract:
Age and Gender are identified as very important attributes in human identification and these attributes are used in various fields of Human and Computer Interaction (HCI) such as security systems, video-surveillance systems, online purchasing systems, judicial systems, transport, medicine, and so many others. In recent years, age and gender estimation based on facial feature analysis have been articulated as a challenging research topic by many researchers in the HCI field. In this research, we aim to present a combined classifier of neural networks with decision fusion for age and gender classification. The novelty of our research is the fusion of the decisions obtained by the two neural networks to increase the accuracy of age and gender estimation. We used the probabilistic decision fusion techniques such as Majority Voting decision fusion, Naïve – Bayes Combination decision fusion and Sum Rule decision fusion for better recognition accuracy rate. Among these technics used, the sum rule decision fusion
