Real-time Face Recognition System Using Deep Learning Method

In this demo session, a real-time automatic face detection and recognition system will be demonstrated. The system, which is implemented as a desktop application with a user interface, detects the faces in the images that are grabbed from a web camera using a cascaded classifier consisting of Modified Census Transform features. Then, using the same method, it locates the eyes and the mouth on each face and uses this information to align the faces. Finally, it recognizes these aligned faces using a novel method called local Zernike moments. In order to improve the detection performance, the system also includes face tracking. The result of the recognition process can be observed by the names that are written by the system near the faces. The system makes it possible to add a previously unseen person to the database easily via its user interface.

Nowadays that security comes into more prominence every day, it is necessary for people to keep more passwords in their mind and carry more cards with themselves. Such implementations however, are becoming less secure and practical, thus leading to an increasing interest in techniques related to biometrics systems. Biometrics systems are the systems which store physical properties of people in electronic environment and enable them to be recognized by the stored electronic information when needed. Biometrics is the identification of human. It works on the principle of identification of physical properties of a person which he or she cannot alter, are distinctive from others, can be used for identification, and are in his or her possession only. Extensive studies are conducted on biometrics techniques such as fingerprint, hand, face, iris, retina and voice recognition. Some systems have been developed, tested, and results have been obtained. Face recognition systems are among the most important subjects in biometrics systems. These systems, which are very important for security in particular, have been widely used and developed in many countries. This study aims to achieve face recognition successfully by detecting human face in real time, based on Principal Component Analysis (PCA) algorithm and comparing the result with pre-recorded face samples.

In this paper, a real time face detection and recognition system is introduced for applications or services in Ubiquitous network environments. The system is realized based on a Haar-like features algorithm and a Hidden Markov model (HMM) algorithm using communications between a WPS(Wearable Personal Station) 350MHz development board and a Pentium III 800 MHz main server communicating with each other by a Bluetooth wireless communication method. Through experimentation, the system identifies faces with 96% accuracy for 480 images of 48 different people and shows successful interaction between the WPS board and the main server for intelligent face recognition service in Ubiquitous network environments.

. In the field of deep learning, facial recognition belongs to the computer vision category. In various applications such as access control system, security, attendance management etc., it has been widely used for authentication and identification purposes. In deep learning, transfer learning is a method of using a neural network model that is first trained on a problem similar to the problem that is being solved. The most commonly used face recognition methods are mainly based on template matching, geometric features based, algebraic and deep learning method. The advantage of template matching is that it is easy to implement, and the disadvantage is that it is difficult to deal with the pose and scale changes effectively. The most important issue, regardless of the method used in the face recognition system, is dimensionality and computational complexity, especially when operating on large databases. In this paper, we applied a transfer learning model based on AlexNet Deep convolutional network to develop a real time face recognition system that has a good robustness to face pose and illumination, reduce dimensionality, complexity and improved recognition accuracy. The system has a recognition accuracy of 98.95 %.

This paper presents two techniques in developing a computation savings Real-Time Face Detection and Recognition System. There have been numerous techniques proposed for face detection and face recognition. Most techniques emphasize on the efficiency in getting positive results, but when come to implementation in a real-time system, speed is vital. We focuses on our proposed methods to speed up and optimize the system components, e.g. the image resolution resizing technique to perform faster face detection in high resolution image, and face tracking to reduce redundant processes in face recognition. Our system testing reveals that the techniques are effective whereby the system is successfully optimized to have improved computation speed especially in a computationally constrained task environment, thus making it compatible for real-time applications.

Power system fault type classification and location prediction is critical in assessing the reliability of the power system, and later restoring it to a stable operating point followed by a fault. State of the art methods include sequence component, impedance measurement from the origin of the fault, and traveling wave based methods for fault type detection and classification problem. It is important to identify and classify the fault as quickly as possible for restoring power system stability to normal operation. Machine Learning and Deep Learning methods allow the analysis of large data of fault voltages and currents by using fast and efficient algorithms. These methods require large amount of data, however, with the recent advances in the field of power system, data acquisition using smart meters and Phasor Measurement Units (PMU), huge amount of system-wide data can be made available to analyze the problem of fault type classification and location prediction. This paper presents a comparative study of Stochastic Gradient Descent (SGD) based Deep Neural Network (DNN) and Machine Learning (ML) applied to power system fault type and location prediction problem. DNN architecture uses 10 hidden layers, each layer having 60 units with hyperbolic tangent as activation function, and a combination of Support Vector Machine (SVM) and Principal Component Analysis (PCA) method are considered. Comparative results in terms of time taken to run the algorithms, and accuracy of the results obtained are presented for a 3 machine 9 bus system. Results indicate that SVM method is an optimal choice for fault classification with high accuracy for location prediction and low computational requirements compared with DNN.

Face Recognition is considered as a heavily studied topic in computer vision field. The capability to automatically identify and authenticate human's faces using real-time images is an important aspect in surveillance, security, and other related domains. There are separate applications that help in identifying individuals at specific locations which help in detecting intruders. The real-time recognition is mandatory for surveillance purposes. A number of machine learning methods along with classifiers are used for the recognition of faces. This work introduces a new real time face Recognition system. The process is broken into four major steps: (1) database collection, (2) face recognition to identify particular persons and (3) Performance evaluation. For the first step, the system collects 1056 faces in real time for 24 persons using a camera with resolution of 112*92.Second step, efficient real time face recognition algorithm is then used to recognize faces with a known database. For real time face Recognition, VGG-16 with Transfer Learning and Convolutional Neural Network (CNN) are used. This proposed system is implemented using keras. Lastly the performance of these two classifiers is measured using of precision, recall, F1-score, and accuracy.

With the widely application of face recognition and the rapid development of Android OS, technique of face detection and recognition based on Android platform becomes increasingly attractive. This paper presents a real-time face recognition system on Android platform. The system realizes face detection by applying AdaBoost algorithm and face recognition by utilizing Eigenfaces. This paper also came up with some methods to speed up the face detection and recognition process and improve the correct rate of face recognition. Experimental results show that this system is able to realize real-time face detection and recognition on Android smart phones. In addition, all the work is completed on the smart phone without using any other terminals or tools.

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The real time face recognition system used to capture the real time face of end user, detect the face, and perform the recognition by preexisting features of the corresponding user. The real time face tracking and recognition is a challenging research problem since last two decades due to the various presence of various illumination conditions, low-resolutions face images, different facial expressions, etc. in capture face images. In this research work, we proposed a novel framework for the real time face tracking and recognition regardless of input facial research dataset. The proposed method is composed of four main steps such as pre-processing, face descriptor, features extraction and selection, and classification. In the first phase, we first perform the task of face tracking and cropping the detected face using the Viola–Jones (VJ) method, we further applied the Gaussian filtering to smooth the cropped face image. In the second phase, the novel face descriptor method called Tracked Directional Ternary Pattern (TDTP) was proposed to address the real time face recognition challenges. In features extraction method, we designed a novel modified DCT (MDCT) based feature extraction method to address the challenge of low-resolution face images.

In general, the field of face recognition has gain a lot of attention for researchers. As the crime increases day by day, the forensic surveillance is required to construct a facial attribute, a visual likeness of a victim's face. Many researchers have contributed their work in this field by using various methods. Real time face detection is still a tedious task and achieve a good performance of the system. In this paper we will compare two models of face recognition. First is traditional method and second is deep learning method on real time dataset. After comparing these methods with different algorithms, will find the accuracy of the models on same dataset and check whether it predict a correct face or not.

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Face detection and recognition system (FDRS) is widely used in modern biometric security systems. In this paper, we implement a new model of FDRS to identify human faces under different poses. Particularly, we propose the Viola-Jones detector to locate and detect human faces and the VanderLugt correlator (VLC) technique for identification. We propose two implementations based on Zync SOC. A set of experiments has been considered using the PHPID database where the faces ranging from -30° to +30°. For more details, see [4].

This article presents a real-time face detection and recognition system for mobile robots based on videos with a complex background. In the visual system, we propose a multi-information method consisting of an Adaboost algorithm, and color information for the face detection part. The interesting targets in the video will first be detected by the Adaboost algorithm, which is robust to illumination. Then the skin color model in YCbCr space will be employed to select the parts that may not be skin areas from the information detected by the Adaboost algorithm. An embedded hidden Markov model (EHMM) is presented, using a 2-DCT feature vector as the observation vector, to recognize the faces detected. The whole process of detecting and recognizing a frame, which is 320 × 240, will take 1.4 s with the rapid recognition parameters and 4.2 s with the slow recognition parameters.

In this paper, a modular, configurable and versatile hardware platform for real-time video and image processing is presented. The hardware platform is based on the Altera DE2 development board which is completed with a Camera interface for video acquisition and a VGA interface for image restitution. A facial recognition system is a computer application for automatically identifying or verifying a person from a digital image or a video frame from a video source. A number of defense, security and commercial applications demand real time face recognition systems, especially when other biometric techniques are not feasible. Using it in our project fulfils the need of car security so as to prevent car thefts which is easier in case of car locking systems. An FPGA (Field Programmable Gated Array) based novel design has been developed which provides real time face tracking of the respective person and if found the respective persons open the lock of the car. The whole system is developed on Altera DE2 board using Verilog HDL (Hardware Description Language) as a coding language. We have not used any of the basic algorithms for the project; we developed our own algorithm by using DE2_TV core provided along with the Altera DE2 board. In our project a fixed background of black color is used and when the pixels of background are distorted by incoming person the corresponding pixels are saved in the SRAM of the board and then later retrieved for face detection and recognition. If the distorting pixels are same with the already saved then the lock of the car opens.