Face detection in the compressed domain

Abstract— In this digital era, face recognition system plays a vital role in almost every sector. Face recognition is one of the mostly used biometrics. It can used for security, authentication, identification, and has got many more advantages. Despite of having low accuracy when compared to iris recognition and fingerprint recognition, it is being widely used due to its contactless and non-invasive process. Furthermore, face recognition system can also be used for attendance marking in schools, colleges, offices, etc. This system aims to build a class attendance system which uses the concept of face recognition as existing manual attendance system is time consuming and cumbersome to maintain. And there may be chances of proxy attendance. Thus, the need for this system increases. This system consists of four phases- database creation, face detection, face recognition, attendance updation. Database is created by the images of the students in class. Face detection and recognition is performed using Haar-Cascade classifier and Local Binary Pattern Histogram algorithm respectively. Faces are detected and recognized from live streaming video of the classroom. Keywords—Face Recognition; Face Detection; Haar-Cascade; Opencv :library are used in face recognition system.

From the past two decades, various spheres of activity are in the aspect of ‘Face recognition’ as an essential tool. The complete series of actions of face recognition is composed of 3 stages: Face Detection, Feature Extraction and Recognition. In this paper, we make an effort to put forth a new application of face recognition and detection in education. The proposed system scans the classroom and detects the face of the students in class and matches the scanned face with the templates that is available in the database and updates the attendance of the respective students.

This project presents a Home Automation Security System that integrates face detection and recognition with RFID technology using a Raspberry Pi. The system aims to enhance security and access control in homes by automating the process of user authentication through facial recognition and RFID tags. The system utilizes a Raspberry Pi as the central control unit, a camera module for face detection and recognition, and an RFID reader to scan RFID tags for additional access control. The system functions by scanning an RFID tag, and upon detection of a valid tag, it activates the camera to capture the face of the person requesting access. The captured image is then processed using OpenCV and face_recognition libraries in Python to match the face with a pre-stored database of authorized individuals. If both the RFID tag and face match the stored data, the system grants access by performing automation actions such as unlocking doors or turning on lights. Keywords- Raspberry Pi, Pi Camera, Face Detection, Face Recognition, RFID Technology, RFID Tag Verification.

Application of Face Recognition Method Under Deep Learning Algorithm in Embedded Systems

The main precondition for applications such as face recognition and face de-identification for privacy protection is efficient face detection in real scenes. In this paper, we propose a hybrid cascade model for face detection in the wild. The cascaded two-stage model is based on the fast normalized pixel difference (NPD) detector at the first stage, and a deep convolutional neural network (CNN) at the second stage. The outputs of the NPD detector are characterized by a very small number of false negative (FN) and a much higher number of false positive face (FP) detections. The FP detections are typically an order of magnitude higher than the FN ones. This very high number of FPs has a negative impact on recognition and/or de-identification processing time and on the naturalness of the de-identified images. To reduce the large number of FP face detections, a CNN is used at the second stage. The CNN is applied only on vague face region candidates obtained by the NPD detector that have an NPD score in the interval between two experimentally determined thresholds. The experimental results on the Annotated Faces in the Wild (AFW) test set and the Face Detection Dataset and Benchmark (FDDB) show that the hybrid cascade model significantly reduces the number of FP detections while the number of FN detections are only slightly increased.

There is an abnormal increase in the crime rate and also the number of criminals are increasing, this leads towards a great concern about the security issues. Crime preventions and criminal identification are the primary issues before the police personnel, since property and lives protection are the basic concerns of the police but to combat the crime, the availability of police personnel is limited. With the advent of security technology, cameras especially CCTV have been installed in many public and private areas to provide surveillance activities. The footage of the CCTV can be used to identify suspects on scene. This Real time criminal identification system based on face recognition works with a fully automated facial recognition system. Haar feature-based cascade classifier and OpenCV LBPH (Local Binary Pattern Histograms) Algorithms are used for Face detection and recognition. This system will be able to detect face and recognize face automatically in real time. An accurate location of the face is still a challenging task. This Framework has been widely used by researchers in order to detect the location of faces and objects in a given image. Face detection classifiers are shared by public communities, such as OpenCV. The automatically tagging feature adds a new dimension to sharing pictures among the people who are in the picture and also gives the idea to other people about who the person is in the image. In our project, we have studied and implemented a pretty simple but very effective face detection using CNN algorithm which takes human skin color into account. Our aim, which we believe we have reached, was to develop a system that can be used by police or investigation department to recognize criminal from their faces. The method of face recognition used is fast, robust, reasonably simple and accurate with a relatively simple and easy to understand algorithms and technique. Key Words: Face Detection, MTCNN, Deep Learning, Real-Time Surveillance, Criminal Identification, CNN

Automatic face detection in video sequences using local normalization and optimal adaptive correlation techniques

Real-time face detection and recognition using hybrid-information extracted from face space and facial features

In order to support real-time face recognition using a wireless camera network, we design a data acquisition service to quickly and reliably acquire face images of human subjects from multiple views and to simultaneously index each acquired image into its corresponding pose. In comparison with detection of frontal faces, the detection of non-frontal faces with unknown pose is a much more challenging problem that involves significant image processing. In this paper, we describe a collaborative approach in which multi-view camera geometry and inter-camera communication is utilized at run time to significantly reduce the required processing time. By doing so, we are able to achieve a high capture rate for both frontal and non-frontal faces and at the same time maintain a high detection accuracy. We implement our face acquisition system on a 1.6 GHz Intel Atom Processor based embedded camera network and show that we can reliably acquire frontal faces at 11 fps and non-frontal faces at 10 fps on images captured at a resolution of 640 × 480 pixels.

In this paper, an automatic face recognition system is proposed based on appearance-based features that focus on the entire face image rather than local facial features. The first step in face recognition system is face detection. Viola-Jones face detection method that capable of processing images extremely while achieving high detection rates is used. This method has the most impact in the 2000’s and known as the first object detection framework to provide relevant object detection that can run in real time. Feature extraction and dimension reduction method will be applied after face detection. Principal Component Analysis (PCA) method is widely used in pattern recognition. Linear Discriminant Analysis (LDA) method that used to overcome drawback the PCA has been successfully applied to face recognition. It is achieved by projecting the image onto the Eigenface space by PCA after that implementing pure LDA over it. Square Euclidean Distance (SED) is used. The distance between two images is a major concern in pattern recognition. The distance between the vectors of two images leads to image similarity. The proposed method is tested on three databases (MUCT, Face94, and Grimace). Different number of training and testing images are used to evaluate the system performance and it show that increasing the number of training images will increase the recognition rate.

Human face detection and recognition is a hot topic and an active area of research. It is common in several fields such as image processing and computer vision. It is the primary and the first step in wide range of applications such as face recognition, personal identification, identity verification, facial expression extraction, and gender classification [1]. In this paper, a multi stage model for face detection is integrated based on Viola and Jones algorithm, Gabor Filters, Principal Component Analysis, and Artificial Neural Networks (ANN). This model was trained and tested using CMU (Carnegie Mellon University) data set [2]. The model showed an enhanced performance in terms of face detection rate.

This paper presents a novel approach for detection and recognition of multi-view faces whose location is unknown and the illumination conditions are varying. The illumination is a big problem in the face detection and recognition aspects. Two completely different methods are proposed for face detection in this paper. Our proposed methods do not use the skin colour information for face detection. The detection of faces is accomplished after cancelling the effect of the various illumination conditions. Because of the independency of the approaches to the face's skin colour, persons with every kind of skin colours are detected even in completely dark environments. Next, the detected faces are recognized. The experiments have shown that we can combine some proposed aspects of feature based methods with eigenface method and get successful results. The illumination dependency and scaling problems of eigenface method have also been solved by a new methodology in this paper. (9 pages)

Fairness has become an important agenda in computer vision and artificial intelligence. Recent studies have shown that many computer vision models and datasets exhibit demographic biases and proposed mitigation strategies. These works attempt to address accuracy disparity, spurious correlations, or unbalanced representations in datasets in tasks such as face recognition, verification and expression and attribute classification. These tasks, however, all require face detection as the first preprocessing step, and surprisingly, there has been little effort in identifying or mitigating biases in face detection. Biased face detectors themselves pose a threat against fair and ethical AI systems, and their biases may be further passed on to subsequent downstream tasks such as face recognition in a computer vision pipeline. This paper therefore investigates the problem of biases in face detection, focusing on accuracy disparity of detectors between demographic groups including gender, age group, and skin tone. We collect perceived demographic attributes on a popular face detection benchmark dataset, WIDER FACE, report skewed demographic distributions, and compare detection performance between groups. In order to mitigate the biases, we apply three mitigation methods that have been introduced in the recent literature and also propose two novel methods. Experimental results show that these methods are effective in reducing demographic biases. We also discuss how the effectiveness varies by demographic attributes, detection easiness, and multiple detectors, which will shed light on this new topic of addressing face detection bias.

Face recognition is among the most productive image processing applications and has a pivotal role in the technical field. In the recent time automated face recognition has become a trend and has been developed very much, this is mainly due to two reasons first it is due to availability of modern technologies and second is due to the ability to save time using face recognition in the process of taking attendance of students. Its usage will grow vast in the future as it saves a lot of time. It consumes a lot of time to take attendance manually and few might also fake the attendance, in order to prevent time consumption and avoid faking the attendance. Face recognition is used to identify the person present in the class and mark their attendance , this is done with the help of image or video frame. We proposed an automatic attendance management system using techniques such as PCA algorithm. The face detection and recognition will automatically detect the students in the classroom and mark the attendance by recognizing the person. The traditional process of making attendance and present biometric systems are vulnerable to proxies. This paper is therefore proposed to tackle all these problems. The proposed system makes the use of Haar classifiers, KNN, CNN, SVM, Generative adversarial networks, and Gabor filters. After face recognition attendance reports will be generated and stored in excel format. The system is tested under various conditions like illumination, head movements, the variation of distance between the student and cameras. After vigorous testing overall complexity and accuracy are calculated. The Proposed system proved to be an efficient and robust device for taking attendance in a classroom without any time consumption and manual work. The system developed is cost-efficient and need less installation

A typical issue in biometry analysis could be face recognition from an images or videos. Since face recognition doesn’t require the cooperation of the object, it plays a crucial role in closed circuit television. The advantages of face-based identification over other biometry are individualism and acceptance as a face can be a dynamic object. Many public spaces regularly have police investigation cameras for video capture. These cameras have their important worth for security. This paper’s objective is to judge the various face detection and recognition techniques and provide a comprehensive solution for image based face detection and recognition with high accuracy and a higher response rate as a first step in video police investigation. The solution is projected supported by tests that were carried out on various face to rich databases in terms of subjects’ ability to evoke emotions and their weight and race.