Human Action Recognition (HAR) involves human activity monitoring task in different areas of medical, education, entertainment, visual surveillance, video retrieval, as well as abnormal activity identification, to name a few. Due to an increase in the usage of cameras, automated systems are in demand for the classification of such activities using computationally intelligent techniques such as Machine Learning (ML) and Deep Learning (DL). In this survey, we have discussed various ML and DL techniques for HAR for the years 2011–2019. The paper discusses the characteristics of public datasets used for HAR. It also presents a survey of various action recognition techniques along with the HAR applications namely, content-based video summarization, human–computer interaction, education, healthcare, video surveillance, abnormal activity detection, sports, and entertainment. The advantages and disadvantages of action representation, dimensionality reduction, and action analysis methods are also provided. The paper discusses challenges and future directions for HAR.

A survey on video-based Human Action Recognition: recent updates, datasets, challenges, and applications

Face recognition technology is a biometric technology, which is based on the identification of facial features of a person. People collect the face images, and the recognition equipment automatically processes the images. The paper introduces the related researches of face recognition from different perspectives. The paper describes the development stages and the related technologies of face recognition. We introduce the research of face recognition for real conditions, and we introduce the general evaluation standards and the general databases of face recognition. We give a forward-looking view of face recognition. Face recognition has become the future development direction and has many potential application prospects.

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A Review of Face Recognition Technology

An android based course attendance system using face recognition

Face recognition is receiving a significant attention due to the need of facing important challenges when developing real applications under unconstrained environments. The three most important challenges are facial occlusion, the problem of dealing with a single sample per subject (SSPS) and facial expression. This paper describes and analyzes various strategies that have been developed recently for overcoming these three major challenges that seriously affect the performance of real face recognition systems. This survey is organized in three parts. In the first part, approaches to tackle the challenge of facial occlusion are classified, illustrated and compared. The second part briefly describes the SSPS problem and the associated solutions. In the third part, facial expression challenge is illustrated. In addition, pros and cons of each technique are stated. Finally, several improvements for future research are suggested, providing a useful perspective for addressing new research in face recognition.

A survey on techniques to handle face recognition challenges: occlusion, single sample per subject and expression

Classroom attendance check is a contributing factor to student participation and the final success in the courses. Taking attendance by calling out names or passing around an attendance sheet are both time-consuming, and especially the latter is open to easy fraud. As an alternative, RFID, wireless, fingerprint, and iris and face recognition-based methods have been tested and developed for this purpose. Although these methods have some pros, high system installation costs are the main disadvantage. The present paper aims to propose a face recognition-based mobile automatic classroom attendance management system needing no extra equipment. To this end, a filtering system based on Euclidean distances calculated by three face recognition techniques, namely Eigenfaces, Fisherfaces and Local Binary Pattern, has been developed for face recognition. The proposed system includes three different mobile applications for teachers, students, and parents to be installed on their smart phones to manage and perform the real-time attendance-taking process. The proposed system was tested among students at Ankara University, and the results obtained were very satisfactory.

Face Recognition-Based Mobile Automatic Classroom Attendance Management System

In this paper we propose an automated attendance management system This system, which is based on face detection and recognition algorithms, automatically detects the student when he enters the class room and marks the attendance by recognizing him The system architecture and algorithms used in each stage are described in this paper Different real time scenarios are considered to evaluate the performance of various face recognition systems This paper also proposes the techniques to be used in order to handle the threats like spoofing When compared to traditional attendance marking this system saves the time and also helps to monitor the students

Automated attendance management system based on face recognition algorithms

Face recognition in real-time scenarios is mainly affected by illumination, expression and pose variations and also by occlusion. This paper presents the framework for pose adaptive component-based face recognition system. The framework proposed deals with all the above mentioned issues. The steps involved in the presented framework are (i) facial landmark localisation, (ii) facial component extraction, (iii) pre-processing of facial image (iv) facial pose estimation (v) feature extraction using Local Binary Pattern Histograms of each component followed by (vi) fusion of pose adaptive classification of components. By employing pose adaptive classification, the recognition process is carried out on some part of database, based on estimated pose, instead of applying the recognition process on the whole database. Pre-processing techniques employed to overcome the problems due to illumination variation are also discussed in this paper. Component-based techniques provide better recognition rates when face images are occluded compared to the holistic methods. Our method is simple, feasible and provides better results when compared to other holistic methods.

Illumination,Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications

Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications

Region of Interest based compression is an efficient method of compression for images with a particular part to be most significant. It is always a better choice to compress the ROI with lossless compression while the rest of image with lossy compression technique. This paper deals with the compression of color images with the face of the person (ID cards, voter ID). This paper proposes lossless compression for face (ROI) and near lossless compression for the rest of the image. Image other than ROI may contain information that is useful, that is why it is appropriate to use near lossless compression for the rest of the image. Face detection suggested in this paper detects the face with the detection of skin color and natural dimensional ratio of height to width of normal human face. In this method of compression, PSNR obtained is remarkable and compression ratio can be increased by increasing the base value which is in power of two. In this method of compression, face will be compressed completely lossless by using Huffman coding and Snake difference scanning is used for achieving a good compression ratio. Proposed algorithm provides quality PSNR with reasonable CR with lossless compression of ROI.

ROI based near lossless hybrid image compression technique

Segmentation and boundary detection is one of the most important task in image processing. In segmentation, an image is partitioned into a set of distinct regions. This is an intermediate step in various image processing applications like content based image and video retrieval, object recognition and tracking, and biomedical engineering etc. In this paper, we implemented an geometric active contour model which uses level set methods and an energy function, by minimizing it we can evolve the boundary of an object. This algorithm does not depend on the gradient of the image, so its performance is good for noisy images also. It can be used to detect lesion or tumor in an MRI image.

https://ieeexplore.ieee.org/iel7/6828197/6839219/06839280.pdf

M. V. Raghunadh

Papers

Automated attendance management system based on face recognition algorithms

Illumination,Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications

Illumination, Expression and Occlusion Invariant Pose-Adaptive Face Recognition System for Real-Time Applications

ROI based near lossless hybrid image compression technique

An application of geometric active contour in bio-medical engineering