![]() ![]() The eyes are the only feature of face that are not greatly affected by certain facial changes, i.e., applying facemasks, and growing hairs, like beard or mustache. Similarly, a face ID framework that endeavors to set up the character of a given individual out of a pool of N individuals is proposed in. A face check framework includes affirming or denying the personality asserted by an individual is proposed in. The variations in the face of a person, due to aging, is one of the main challenges in the person’s identification process. However, face recognition involves several challenges. An additional field of application of face recognition can be criminal prosecution in public places using the image data of surveillance cameras. In the transfer of knowledge, we first train a target model on a sample dataset and then reuse the acquired features or shift them to the targeted system to be built on a model domain. Transfer learning tries to expand on old-fashioned machine learning by transferring knowledge cultured in one or additional basis responsibilities and expending it to expand learning in an associated mark job. Transfer learning is the enhancement of learning in an innovative mission through the transmission of information from an associated job that has previously been cultured. ![]() For example, the knowledge gained from the face identification during a live broadcast of a sport event can be applied to identify a person at the airport. Transfer learning is a computer vision methodology in which the knowledge gained while solving a problem can be applied to a different, but related, problem, to obtain a more efficient solution. Recognition of person’s face is a stimulating task that plays a significant role in several areas, like preventing retail crime, enforcement of laws, banking security and scrutiny systems, accessing unlocked phones and most significantly for personal evidence. Object recognition is the process of recognizing a definite entity, such as faces, number plates of different vehicles such as cars in a given image or a video sequence. The experimental results of this study show that faces are recognized accurately and LBPH method has achieved 98.2% accuracy. The algorithm finds the brighter eye from the face and then, on the basis of that eye, the person is identified and the name of person is provided. This study is able to identify an individual on the basis of even a single eye. Furthermore, neighborhood pixels are calculated to extract effective facial feature to realize eyes recognition and person verification. Extracted Local Binary Pattern Histogram (LBPH) method is used to extract the facial features of face images whose computational complexity is very low and these features contain simple pixel values. The key features for eyes recognition are center of left eye, center of right eye, midpoint of eyes and extraction of eyebrows. The key features for face recognition, used in this study are the eyes, nostrils, and mouth. ![]() The contributions of this work are divided into three parts, specifically face detection, eyes detection and recognition for individual identification. ![]() In this paper, we show that we can obtain promising results on the standard face databanks when the features are extracted merely from the eye. Face detection and recognition are the most substantial research areas in computer vision and transfer learning due to the inspiring nature of faces as an object. ![]()
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