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Face recognition is a biometric grounded technology that mathematically charts particular person’s or individual’s facial features and stores all that data as a face print. By using this fashion, the information of the face of a person is saved mathematically or in the format of graphs in the database, which is used for detecting that particular face. Face recognition model in our system will find a match of that person in the database. If a match is found, it will be notified to the police and the guardian of that person. The face recognition model in our system will try to find a match in the database with the help of Tensor Flow Face recognition algorithm. It is performed by comparing the face encodings of the uploaded image to the face encodings of the images in the database. If a match is found, it will be notified to the police and the people related to that person along with the position of where the person is found. Face recognition models in Deep and Machine Learning are primarily created to ensure the security of identity. There are several frameworks used in building a face recognition model and one of them is Tensor Flow. The Tensor Flow face recognition model has so far proven to be popular. Using Tensor Flow to build face recognition and discovery models might bear trouble, but it is worth it in the end. As mentioned, Tensor Flow is the most used Deep Literacy framework and it has pre-trained models that fluently help with image bracket.
Arunvasu et al. (Fri,) studied this question.