Intelligent Voting System with Face Recognition by HAAR Cascade Algorithm

Computer Science

This article presents a human face detection method for voting systems to match features in images accordingly that correspond to lines on the same viewpoint, image. Biometric software that works by matching features of face such as eyes, nose lines, and markings. Facial recognition has always been a very much secure and trusted form of identification. By using this functionality into current voting system, we can enhance the system's abilities to make it safer and less fraudulent. This article provided his one algorithm, HAAR Cascade.. We can also compared performance by classifying images of faces. In our proposed system we have 1900 images. Training set’s image have been augmented to further improve their properties. Each and every augmented set consists of 4 and more samples per image. Our completed set consists of 1900*4, or 7600 images. Based on our team’s research work, we have founded that our algorithm's accuracy is based on training data. Our training data have consist of 1900 marked images. For further work, we are planning to extend the training data set and apply other exclusive techniques such as deep learning, neural networks etc.

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