FACE RECOGNITION USING CNN AND EMOTION DETECTION USING LOCAL BINARY PATTERN

FACE RECOGNITION USING CNN AND EMOTION DETECTION USING LOCAL BINARY PATTERN

¹ R.KOWSHIKA, ² J.THILAGAVATHY

¹Student ²Assistant Professor

Grace College Of Engineering,Thoothukudi

ABSTRACT

Facial Recognition possess the importance to give biometric authentication that is used in different applications especially in security. A stored database of the subjects is manipulated using image processing techniques to accomplish this task. This paper proposes a frame work of smart glasses that can recognize the faces. Implementing facial recognition using portable smart glasses can aid law enforcement agencies to detect a suspect's face. The advantage over security cameras is their portability and good frontal view capturing. The techniques used for the whole process of face recognition are machine learning based because of their high accuracy as compared with other techniques. Face detection is the pre-step for face recognition that is performed using Haar-like features. Detection rate of this method is 98% using 3099 features. Face recognition is achieved using Deep Learning's sub-field that is Convolutional Neural Network (CNN). It is a multi-layer network trained to perform a specific task using classification. Transfer learning of a trained CNN model that is AlexNet is done for face recognition. It has an accuracy of 98.5% using 2500 variant images in a class. These smart glasses can serve in the security domain for the authentication process.After that emotion is detected using local binary pattern algorithm,Emotion recognition is already widely used by different companies to gauge consumer mood towards their product or brand. The opportunities brought by this technology goes further than market research and digital advertising.