Chatgpt and Biometrics: an Assessment of Face Recognition, Gender Detection, and Age Estimation Capabilities

Class attendance is a crucial indicator of students' seriousness towards learning. Many institutions continue to use manual methods, which are usually error-prone and unproductive. By leveraging computer vision algorithms, the system accurately captures and verifies the identity of students attending class. This paper aims to investigate and create an automated facial recognition system for classroom attendance to increase the precision and effectiveness of the attendance tracking system. To achieve this, we propose a system using computer vision technologies, namely Histogram of Oriented Gradients (HOG) with Support Vector Machine (SVM) for face detection and deep Convolutional Neural Networks (CNN) for face identification. The facial recognition system simplifies attendance recording, requiring participants to only gaze into the camera for the system to record their presence automatically. The system is rigorously tested and evaluated, and its accuracy is compared to our institution's current QR code attendance method. The study results reveal that the recommended approach is more accurate and competent than the existing procedures. The system allows for precise attendance records with real-time face detection and recognition capabilities. This technology ensures accurate and reliable attendance data, empowering organizations to make informed decisions, effectively manage resources, and provide a seamless experience for all students. In addition, a similar attendance system can be deployed for any event in an organization, thereby enhancing overall operational efficiency.

This paper describes the design and development of an iOS app for selfie search, which combines face detection and recognition capabilities with content-based image retrieval techniques. The app works offline, since all processing takes place entirely on the device. It was implemented in Objective-C and it leverages functionality from Apple's Core Image API for image processing tasks and CouchbaseLite for the database layer. For face recognition, the app employs local binary patterns -- encoded as spatially enhanced histograms, with weight maps that indicate preferred areas within the cropped image containing the face. The source code is available on GitHub.

This project planned to demonstrate the leverage of enhanced computational infrastructure for law enforcement by demonstrating the face recognition capability at LLNL. The project implemented a face finder module extending the segmentation capabilities of the current face recognition so it was capable of processing different image formats and sizes and create the pilot of a network-accessible image database for the demonstration of face recognition capabilities. The project was funded at $40k (2 man-months) for a feasibility study. It investigated several essential components of a networked face recognition system which could help identify, apprehend, and convict criminals.

A surveillance system is still the most exciting and practical security system to prevent crime effectively. The primary purpose of this system is to recognize the identity of the face caught by the camera. With the advancement of the Internet of things, surveillance systems were implemented on edge devices such as the low-cost Raspberry mobile camera. It raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. The challenge is increasing because people used to wear a mask during the Covid -19 pandemic. Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. This system integrated three modules: MTCNN face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker to create a system capable of tracking the faces of people caught on surveillance camera. We also train new face mask data to recognize and track. This system obtains data from the Raspberry Pi camera and processes images on the cloud as a mobile sensor approach. The proposed system successfully implemented and obtained competitive results in detection, recognition, and tracking under an unconstrained surveillance camera.

Face recognition at a distance is gaining wide attention in order to augment the surveillance systems with face recognition capability. However, face recognition at a distance in nighttime has not yet received adequate attention considering the increased security threats at nighttime. We introduce a new face image database, called Near-Infrared Face Recognition at a Distance Database (NFRAD-DB). Images in NFRAD-DB are collected at a distance of up to 60 meters with 50 different subjects using a near-infrared camera, a telescope, and near-infrared illuminator. We provide face recognition performance using FaceVACS, DoG-SIFT, and DoG-MLBP representations. The face recognition test consisted of NIR images of these 50 subjects at 60 meters as probe and visible images at 1 meter with additional mug shot images of 10,000 subjects as gallery. Rank-1 identification accuracy of 28 percent was achieved from the proposed method compared to 18 percent rank-1 accuracy of a state of the art face recognition system, FaceVACS. These recognition results are encouraging given this challenging matching problem due to the illumination pattern and insufficient brightness in NFRAD images.

Face recognition has recently been a popular study topic as a result of the growing demand for security. Security systems, identity verification, access control, surveillance systems, and social networks are just a few of the applications that can be expanded from facial recognition. However, as passwords and fingerprint scanners are supplanted by improved computer technology and more precise algorithms, facial recognition is becoming increasingly popular. Facial recognition technology is the simplest and most coherent of all the techniques. Face recognition does not necessitate an individual’s active participation. Face recognition systems are theoretically capable of minimizing risk and eventually avoiding future attacks in places where identity identification is necessary, such as airports and border crossings. If there are criminals on the loose, the same justification can be made as with surveillance devices. Surveillance cameras with face recognition capabilities will aid in the identification of these individuals. Alternatively, these surveillance systems can be used to track down the whereabouts of missing persons, although this is contingent on the use of powerful facial recognition algorithms and a fully built facial database. Face detection, facial feature extraction (image normalization), face identification, and results are the major operations to be conducted for face recognition.

Improving significantly in the last several years, technologies that can mimic or improve human abilities to recognize and read faces are now maturing for use in medical and security applications. The 2002 Face Recognition Vendor Test (FRVT 2002) demonstrated a significant improvement in face recognition capabilities, and researchers have developed systems to tackle some of face recognition's more interesting challenges. These systems include one that can distinguish between identical twins.

Face recognition capabilities have recently made extraordinary leaps. Though this progress is at least partially due to ballooning training set sizes – huge numbers of face images downloaded and labeled for identity – it is not clear if the formidable task of collecting so many images is truly necessary. We propose a far more accessible means of increasing training data sizes for face recognition systems: Domain specific data augmentation. We describe novel methods of enriching an existing dataset with important facial appearance variations by manipulating the faces it contains. This synthesis is also used when matching query images represented by standard convolutional neural networks. The effect of training and testing with synthesized images is tested on the LFW and IJB-A (verification and identification) benchmarks and Janus CS2. The performances obtained by our approach match state of the art results reported by systems trained on millions of downloaded images.

The overarching goal of the FaceBots project is to support the achievement of sustainable long-term human-robot relationships through the creation of robots with face recognition and natural language capabilities, which exploit and publish online information, and especially social information available on Facebook, and which achieve two significant novelties. The underlying experimental hypothesis is that such relationships can be significantly enhanced if the human and the robot are gradually creating a pool of episodic memories that they can co-refer to (“shared memories”), and if they are both embedded in a social web of other humans and robots they mutually know (“shared friends”). We present a description of system architecture, as well as important concrete results regarding face recognition and transferability of training, with training and testing sets coming from either one or a combination of two sources: an onboard camera which can provide sequences of images, as well as facebook-derived photos. Furthermore, early interaction-related results are presented, and evaluation methodologies as well as interesting extensions are discussed.

Modern autonomous robots must integrate multiple perceptual and behavioral modalities to be useful in our daily lives. Such integration is constrained by the limited onboard computing capacity of robotic platforms. To alleviate this issue, perceptual filtering, a selective attention mechanism, can be used to efficiently manage computing resources based on what the robot has to accomplish. This paper describes our implementation of perceptual filtering in a robot control architecture, implemented using robot operating system (ROS), and how it can dynamically optimize the use of the computing resources available on the robot. Our perceptual filtering mechanism is demonstrated and validated using a mobile humanoid platform integrating autonomous and teleoperated navigation, QR code recognition, face recognition, and sound localization capabilities.

Our project aims at supporting the creation of sustainable and meaningful longer-term human-robot relationships through the creation of embodied robots with face recognition and natural language dialogue capabilities, which exploit and publish social information available on the web (Facebook). Our main underlying experimental hypothesis is that such relationships can be significantly enhanced if the human and the robot are gradually creating a pool of shared episodic memories that they can co-refer to (shared memories), and if they are both embedded in a social web of other humans and robots they both know and encounter (shared friends). In this paper, we are presenting such a robot, which as we will see achieves two significant novelties.

With the maturity of face recognition and speech recognition technologies, artificial intelligence (AI) cloud and edge computing collaboration have become a new research direction. In many enterprises and government departments, there are certain management requirements for visitors, usually using traditional manual records or computer-aided manual management. These methods require certain personnel management costs, and they face underlying problems concerned with personal identification as well as security. In this paper, we analyze the functions and features of cloud-edge collaboration and discuss the edge intelligence technology in the cloud-edge collaboration environment. Then by combining the architecture of the intelligent visitor system, we apply AI cloud and edge computing to collaboratively solve critical issues faced by the visitor system, such as real-time and data authenticity. The intelligent visitor system employs a Rockchip RK3399 motherboard, ID card reader, microphone array, camera, and other hardware to build an edge computing environment. Combined with Baidu AI cloud, the system has an intelligent visitor system with face recognition and voice interaction capabilities, which can realize verification of visitor information, voice self-registration, and automatic measurement of visitors’ body temperature and other functions.

Systems and applications embedded with facial detection and recognition capabilities are founded on the notion that there are differences in face structures among individuals, and as such, we can perform face-matching using the facial symmetry. A widely used application of facial detection and recognition is in security. It is important that the images be processed correctly for computer-based facial recognition, hence, the usage of efficient, cost-effective algorithms and a robust database. This research work puts these measures into consideration and attempts to determine a cost-effective and reliable algorithm out of three algorithms examined.
 Keywords: Haar-Cascade, PCA, Eigenfaces, Fisherfaces, LBPH, Face Recognition.

Face Recognition and Face detection is one of the most challenging problem in today’s era in the field of Image processing and Computer vision. Various algorithms are introduced using convolutional architecture for face detection using technologies like deep learning and machine learning. Due to this pandemic situation of COVID-19, Face mask detection has become a necessity and also an emerging Research in the field of Computer vision. As the situation in all over India is getting worse and death rates are increasing, so for protection wearing mask has become a necessity for each individual during this pandemic situation. This paper proposes the novel approach of utilizing cloud capabilities for face recognition. This paper then compares and analyse of face detection approach required during COVID-19 using the cloud computing techniques like Google cloud vision API, Amazon Face Rekognition, IBM cloud and Kairos facial recognition. As this Cloud computing techniques are automated there is no need of Deep learning and machine learning expert and it will help in faster computing and getting the results faster by using this cloud platforms.

Public preference for data privacy – A pan-European study on metro/train surveillance