Published Paper Details:

The person's face is a highly complicated visual object and the creation of a theoretical model for understanding it is thus very difficult. Photo-based on close twin-face recognition is a difficult challenge in computer technology. Twin Face detection and differentiation is the biggest issue faced by multifaceted graphical model design, and it is a current research topic. Traditional facial recognition program demonstrates low performance under realistic conditions in differentiating identical twins. There are several different strategies for identifying identical twins ex: Fingerprint Identification, Iris Detection, etc. Traditionally, many studies were carried out to distinguish twins and also to recognize their traits of distinction, and there were even other methods to display similarities between twins using fingerprints, speech, and iris as part of pattern recognition. Existing techniques are used to recognize twins such as fingerprinting, speech recognition, and iris. The art of identifying the difference between facial features of twins is very tricky because they display many similar characteristics such as gestures, age, hairstyle change, etc. Twin Face differentiation plays a key role in systems such as surveillance network, authentication of credit cards, identification of offenders at airports, train stations, etc. As most methods for detecting and recognizing the twin faces have been proposed, developing a computer model for a similar image database is still a difficult challenge. It's why twin facial recognition is considered a major-level function in image processing, in which strategies can be developed to produce reliable data. Artificial neural networks, and principal component analysis are a few common methods used for twin-face recognition. In this paper, we are going to use Voila-Jones, Principal Component Analysis (PCA), Euclidean Distance, and Neural Network pattern recognition techniques to differentiate between the twins.