Face Analysis Using CNN-UM (original) (raw)

Design of a Face Recognition System based on Convolutional Neural Network (CNN)

Engineering, Technology & Applied Science Research

Face recognition is an important function of video surveillance systems, enabling verification and identification of people who appear in a scene often captured by a distributed network of cameras. The recognition of people from the faces in images arouses great interest in the scientific community, partly because of the application interests but also because of the challenge that this represents for artificial vision algorithms. They must be able to cope with the great variability of the aspects of the faces themselves as well as the variations of the shooting parameters (pose, lighting, haircut, expression, background, etc.). This paper aims to develop a face recognition application for a biometric system based on Convolutional Neural Networks. It proposes a structure of a Deep Learning model which allows improving the existing state-of-the-art precision and processing time.

Centrally controlled on-chip criminal face recognition embedded in traffic cameras

International journal of health sciences

This paper evaluates the ability of convolutional networks to solve the problems arising with face classification in a constrained environment. It has the design and implementation of Siamese architecture used for face verification using a single set of the photograph. Because of the intrinsic nature of the problem, computer vision is not only a computer science area of research, but also the object of neuroscientific and psychological studies, mainly because of the general opinion that advances in computer image processing and understanding research will provide insights into how our brain works and vice versa. In the scope of the paper, the training process is closely monitored and we evaluate several practices and parameters as well as their impact on network learning. This paper introduces some novel models for all steps of a face recognition system using embedded computers. In the step of single-shot face recognition, we propose a hybrid model combining Openface artificial neur...

A Convolutional Neural Network (CNN) Approach to Detect Face Using Tensorflow and Keras

Other Information Systems & eBusiness eJournal, 2019

Face recognition is used in a variety of aspects in the modern world. Face detection means to identify the face from a digital image. The deep neural network is considered a powerful tool as it can handle huge amounts of data .conventional neural network is one most popular tool to detect face detection. In this paper, a deep convolutional neural network (CNN) to extract features from input images. Keras is used for implementing CNN also D'lib and OpenCV for aligning faces on input images. Face recognition performance is evaluated using a custom dataset.

Face Recognition Using the Convolutional Neural Network for Barrier Gate System

International Journal of Interactive Mobile Technologies (ijim), 2021

The implementation of face recognition technique using CCTV is able to prevent unauthorized person enter the gate. Face recognition can be used for authentication, which can be implemented for preventing of criminal incidents. This re-search proposed a face recognition system using convolutional neural network to open and close the real-time barrier gate. The process consists of a convolutional layer, pooling layer, max pooling, flattening, and fully connected layer for detecting a face. The information was sent to the microcontroller using Internet of Thing (IoT) for controlling the barrier gate. The face recognition results are used to open or close the gate in the real time. The experimental results obtained average error rate of 0.320 and the accuracy of success rate is about 93.3%. The average response time required by microcontroller is about 0.562ms. The simulation result show that the face recognition technique using CNN is highly recommended to be implemented in barrier gat...

A Real-Time Framework for Human Face Detection and Recognition in CCTV Images

Mathematical Problems in Engineering, 2022

This paper aims to develop a machine learning and deep learning-based real-time framework for detecting and recognizing human faces in closed-circuit television (CCTV) images. The traditional CCTV system needs a human for 24/7 monitoring, which is costly and insufficient. The automatic recognition system of faces in CCTV images with minimum human intervention and reduced cost can help many organizations, such as law enforcement, identifying the suspects, missing people, and people entering a restricted territory. However, image-based recognition has many issues, such as scaling, rotation, cluttered backgrounds, and variation in light intensity. This paper aims to develop a CCTV image-based human face recognition system using different techniques for feature extraction and face recognition. The proposed system includes image acquisition from CCTV, image preprocessing, face detection, localization, extraction from the acquired images, and recognition. We use two feature extraction alg...