Face Recognition Using LSTM in Image Processing
I.T. Akshyaa Raaja Shri
Keywords: Face Recognition, Long Short-Term Memory, Image Recognition, Real-Time Face Recognition.
The project has entitled as “FACE RECOGNITION USING LSTM IN IMAGE PROCESSING”, and developed by using Python as front end. A computer vision technique called face detection aids in finding and visualizing human faces in digital images. This method deals with finding instances of semantic objects of a particular class in digital photos and videos. It is a special application of object detection technology. Face identification has become increasingly important with the development of technology, particularly in industries like photography, security, and marketing. This project is designed to detect the human face appearing in front of webcam. It recognizes the face and compares the same face with the dataset. If the face trained before appearing on screen the accuracy will be high (> 90%) otherwise the accuracy will be low for new faces (< 40%). This face detection process is done by using LSTM technique. It helps to increases the accuracy that existing system. The training time required is very less when compared to exiting system. Before applying LSTM, it will take more time to train the face. Here the face detection accuracy level is increases gradually and time requirement is reduced more. A picture is nothing more than a typical NumPy array with pixels representing data points. The resolution of an image improves with increasing pixel count. The depth of a pixel relates to the colour information it contains, and you may think of pixels as tiny blocks of information organised in the shape of a 2D grid. An image must be translated into a binary format before it can be processed by a computer.