Data Mining - Concepts and Techniques.

Over the past few decades, interest in theories and algorithms for face recognition has been growing rapidly. Video surveillance, criminal identification, building access control, and unmanned and autonomous vehicles are just a few examples of concrete applications that are gaining attraction among industries. Various techniques are being developed including local, holistic, and hybrid approaches, which provide a face image description using only a few face image features or the whole facial features. The main contribution of this survey is to review some well-known techniques for each approach and to give the taxonomy of their categories. In the paper, a detailed comparison between these techniques is exposed by listing the advantages and the disadvantages of their schemes in terms of robustness, accuracy, complexity, and discrimination. One interesting feature mentioned in the paper is about the database used for face recognition. An overview of the most commonly used databases, including those of supervised and unsupervised learning, is given. Numerical results of the most interesting techniques are given along with the context of experiments and challenges handled by these techniques. Finally, a solid discussion is given in the paper about future directions in terms of techniques to be used for face recognition.

https://www.mdpi.com/1424-8220/20/2/342/pdf

Face Recognition Systems: A Survey

The ability to predict student performance in a course or program creates opportunities to improve educational outcomes. With effective performance prediction approaches, instructors can allocate resources and instruction more accurately. Research in this area seeks to identify features that can be used to make predictions, to identify algorithms that can improve predictions, and to quantify aspects of student performance. Moreover, research in predicting student performance seeks to determine interrelated features and to identify the underlying reasons why certain features work better than others. This working group report presents a systematic literature review of work in the area of predicting student performance. Our analysis shows a clearly increasing amount of research in this area, as well as an increasing variety of techniques used. At the same time, the review uncovered a number of issues with research quality that drives a need for the community to provide more detailed reporting of methods and results and to increase efforts to validate and replicate work.

https://dl.acm.org/doi/pdf/10.1145/3293881.3295783

Predicting academic performance: a systematic literature review

A new architecture of intelligent audio emotion recognition is proposed in this paper. It fully utilizes both prosodic and spectral features in its design. It has two main paths in parallel and can recognize 6 emotions. Path 1 is designed based on intensive analysis of different prosodic features. Significant prosodic features are identified to differentiate emotions. Path 2 is designed based on research analysis on spectral features. Extraction of Mel-Frequency Cepstral Coefficient (MFCC) feature is then followed by Bi-directional Principle Component Analysis (BDPCA), Linear Discriminant Analysis (LDA) and Radial Basis Function (RBF) neural classification. This path has 3 parallel BDPCA + LDA + RBF sub-paths structure and each handles two emotions. Fusion modules are also proposed for weights assignment and decision making. The performance of the proposed architecture is evaluated on eNTERFACE’05 and RML databases. Simulation results and comparison have revealed good performance of the proposed recognizer.

A new approach of audio emotion recognition

This paper presents a novel facial expression recognition (FER) technique based on support vector machine (SVM) for the FER Here it is called the FERS technique First, the FERS technique develops a face detection method that combines the Haar-like features method with the self-quotient image (SQI) filter As a result, the FERS technique possesses better detection rate because the face detection method gets more accurate in locating face regions of an image The main reason is that the SQI filter can overcome the insufficient light and shade light Subsequently, three schemes, the angular radial transform (ART), the discrete cosine transform (DCT) and the Gabor filter (GF), are simultaneously employed in the design of the feature extraction for facial expression in the FERS technique More specifically, they are employed in constructing a set of training patterns for the training of an SVM The FERS technique then exploits the trained SVM to recognize the facial expression for a query face image Finally, experimental results show that the recognition performance of the FERS technique can be better than that of other existing methods under consideration in the paper

Facial expression recognition using a combination of multiple facial features and support vector machine

Recognition of emotions from speech is one of the most important sub domains in the field of affective computing. Six basic emotional states are considered for classification of emotions from speech in this work. In this work, features are extracted from audio characteristics of emotional speech by Mel-frequency Cepstral Coefficient (MFCC), and Subband based Cepstral Parameter (SBC) method. Further these features are classified using Gaussian Mixture Model (GMM). SAVEE audio database is used in this work for testing of Emotions. In the experimental results, SBC method out performs with 70% in recognition compared to 51% of recognition in MFCC algorithm.

Emotion recognition in speech using MFCC and wavelet features

A novel face recognition system based on combining eigenfaces with fisher faces using wavelets

In recent developments, Educational institutions seeking and persisting best performance from the students. So the Educational Institutions are striving to enhance the performance of students in academics. In this paper a decision tree induction approach called Multivariate Regression prediction model M5P has been used for predicting performance of students based on relevant terms such as online-learning skills, problem-solving efficiency, time management, intention of doing higher studies, adaptable nature, sports participation, day-scholar, self-learning, time spent on social networks, versatility nature, library usage, and practical knowledge. For evaluating the proposed model sample data is collected from 307 students of third year Computer Science and Engineering under graduates, in India. The outcome shown in this proposed model yields better accuracy with 97.17% when compared with other four models. With this model performance of students can be predicted early in the semester and necessary remedial measures can be taken to improve the performance of students.

M5P model tree in predicting student performance: A case study

Classification of facial expressions is a great challenge in the area of computer vision. Using single feature models, the percentage of recognition is considerably low even in controlled conditions of capturing. To improve the accuracy of the facial emotion recognition and classification, a new method is proposed in this paper based on the fusion of features extracted from different techniques. Local features are extracted using Directional Local Binary Patterns (DLBP) and global features are extracted using Discrete Cosine Transform (DCT) from facial expression images. Principal Components Analysis (PCA) is used to reduce the dimensions of extracted features. Weighted summation and PCA fusion methods are used to fuse the local and global features extracted from facial images. RBF neural network is used as classifier for classification of facial images into six emotions (surprise, fear, sad, joy, anger and disgust). Cohn-Kanade database is used to evaluate the proposed method. The results of proposed algorithm yield better recognition rate of 97% in comparison with existing methods.

Multi-Feature Fusion based Facial Expression Classification using DLBP and DCT

Although many approaches for facial expression recognition have been proposed in the past, most of them yielding poor recognition performance with single feature extraction method The objective of this paper is to propose an innovative method based on fusion of local and global features for better classification rate Gabor wavelets(GWT) are used to extract Local features and Discrete Cosine Transform (DCT) is used to extract global features from facial expression images To reduce dimensionality of extracted features and better classification performance Kernel Principal Components Analysis (KPCA) is applied Wavelet fusion method is used to fuse the features extracted from GWT and DCT Finally the images are classified into 6 different basic emotions like surprise, fear, sad, joy, anger and disgust using Radial Basis Function(RBF) Neural Network classifier The performance of the proposed method is evaluated on Cohn-Kanade database The results of proposed algorithm exhibit high performance rate of about 99% in person dependent facial expression recognition

http://ieeexplore.ieee.org/iel7/6713383/6724108/06724211.pdf

K. V. Krishna Kishore

Papers

Emotion recognition in speech using MFCC and wavelet features

A novel face recognition system based on combining eigenfaces with fisher faces using wavelets

M5P model tree in predicting student performance: A case study

Multi-Feature Fusion based Facial Expression Classification using DLBP and DCT

Facial expression classification using Kernel based PCA with fused DCT and GWT features