A face recognition system using convolutional feature extraction with linear collaborative discriminant regression classification

Face recognition is one of the important biometric authentication research areas for security purposes in many fields such as pattern recognition and image processing. However, the human face recognitions have the major problem in machine learning and deep learning techniques, since input images vary with poses of people, different lighting conditions, various expressions, ages as well as illumination conditions and it makes the face recognition process poor in accuracy. In the present research, the resolution of the image patches is reduced by the max pooling layer in convolutional neural network (CNN) and also used to make the model robust than other traditional feature extraction technique called local multiple pattern (LMP). The extracted features are fed into the linear collaborative discriminant regression classification (LCDRC) for final face recognition. Due to optimization using CNN in LCDRC, the distance ratio between the classes has maximized and the distance of the features inside the class reduces. The results stated that the CNN-LCDRC achieved 93.10% and 87.60% of mean recognition accuracy, where traditional LCDRC achieved 83.35% and 77.70% of mean recognition accuracy on ORL and YALE databases respectively for the training number 8 (i.e. 80% of training and 20% of testing data).

A Face Recognition Method in the Internet of Things for Security in Smart Recognition Places

In recent years, the safety constitutes the foremost necessary section of the human life. At this point, the price is that the greatest issue. This technique is incredibly helpful for reducing the price of watching the movement from outside. During this paper, a period of time recognition system is planned which will equip for handling pictures terribly quickly. The most objective of this paper is to safeguard home, workplace by recognizing individuals. The face is that the foremost distinctivea part of human’s body. So, it will replicate several emotions of associate degree Expression. A few years past, humans were mistreatment the non-living things like good cards, plastic cards, PINS, tokens and keys for authentication, and to urge grant access in restricted areas like ISRO, National Aeronautics and Space Administration and DRDO. The most necessary options of the face image are Eyes, Nose and mouth. Face detection and recognition system is simpler, cheaper, a lot of accurate, process. The system under two categories one is face detection and face recognition. Throughout this case, among the paper, the Raspberry Pi single-board computer is also a heart of the embedded face recognition system. Keywords: Raspberry Pi, Face recognition system

Thermal Image Generation for Robust Face Recognition

This article shows how to create a robust thermal face recognition system based on the FaceNet architecture. We propose a method for generating thermal images to create a thermal face database with six different attributes (frown, glasses, rotation, normal, vocal, and smile) based on various deep learning models. First, we use StyleCLIP, which oversees manipulating the latent space of the input visible image to add the desired attributes to the visible face. Second, we use the GANs N’ Roses (GNR) model, a multimodal image-to-image framework. It uses maps of style and content to generate thermal imaging from visible images, using generative adversarial approaches. Using the proposed generator system, we create a database of synthetic thermal faces composed of more than 100k images corresponding to 3227 individuals. When trained and tested using the synthetic database, the Thermal-FaceNet model obtained a 99.98% accuracy. Furthermore, when tested with a real database, the accuracy was more than 98%, validating the proposed thermal images generator system.

The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances

Face recognition is one of the most popular and long-standing topics in computer vision. With the recent development of deep learning techniques and large-scale datasets, deep face recognition has made remarkable progress and been widely used in many real-world applications. Given a natural image or video frame as input, an end-to-end deep face recognition system outputs the face feature for recognition. To achieve this, a typical end-to-end system is built with three key elements: face detection, face alignment, and face representation. The face detection locates faces in the image or frame. Then, the face alignment is proceeded to calibrate the faces to the canonical view and crop them with a normalized pixel size. Finally, in the stage of face representation, the discriminative features are extracted from the aligned face for recognition. Nowadays, all of the three elements are fulfilled by the technique of deep convolutional neural network. In this survey article, we present a comprehensive review about the recent advance of each element of the end-to-end deep face recognition, since the thriving deep learning techniques have greatly improved the capability of them. To start with, we present an overview of the end-to-end deep face recognition. Then, we review the advance of each element, respectively, covering many aspects such as the to-date algorithm designs, evaluation metrics, datasets, performance comparison, existing challenges, and promising directions for future research. Also, we provide a detailed discussion about the effect of each element on its subsequent elements and the holistic system. Through this survey, we wish to bring contributions in two aspects: first, readers can conveniently identify the methods which are quite strong-baseline style in the subcategory for further exploration; second, one can also employ suitable methods for establishing a state-of-the-art end-to-end face recognition system from scratch.

RFaceID

Face recognition (FR) has been widely used in many areas nowadays. However, the existing mainstream vision-based facial recognition has limitations such as vulnerability to spoofing attacks, sensitivity to lighting conditions, and high risk of privacy leakage, etc. To address these problems, in this paper we take a sparkly different approach and propose RFaceID, a novel RFID-based face recognition system. RFaceID only needs the users to shake their faces in front of the RFID tag matrix for a few seconds to get their faces recognized. Through theoretical analysis and experiment validations, the feasibility of the RFID-based face recognition is studied. Multiple data processing and data augmentation techniques are proposed to minimize the negative impact of environmental noises and user dynamics. A deep neural network (DNN) model is designed to characterize both the spatial and temporal feature of face shaking events. We implement the system and extensive evaluation results show that RFaceID achieves a high face recognition accuracy at 93.1% for 100 users, which shows the potential of RFaceID for future facial recognition applications.

Research on Tourism Route Planning Based on Artificial Intelligence Technology

Study on designing reasonable travel routes with the least time cost and the highest experience index was conducted. An artificial intelligence-based wireless sensor travel route planning study is proposed. First, the improved TSP route planning model is built at the least time consumption and combines the normal distributed random number (ND) with the genetic algorithm (GA) and proposes the ND-GA algorithm, analyzes the overall structure, node structure, communication mode, and network coverage of the wireless sensor network, and gives a mathematical model of wireless transmission energy consumption. Using the proposed algorithm to solve the travel route and detailed itinerary, with time, the 10-year travel route design model based on multitarget dynamic optimization finally detailed analysis of the model results and sensitivity analysis results showing that the application of AI wireless sensor technology can also make the scenic work more efficient; for example, a face recognition system can improve the speed of ticket checking. Although the application of AI technology is widely used in tourism activities, there are some problems, which require the continuous optimization and innovation of AI wireless sensor technology by relevant practitioners, so that it can better serve tourists.