Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

International Journal of Computer and Applications

Internet of Things: A survey on machine learning-based intrusion detection approaches

Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review

Automatic emotion recognition has become a trending research topic in the past decade. While works based on facial expressions or speech abound, recognizing affect from body gestures remains a less explored topic. We present a new comprehensive survey hoping to boost research in the field. We first introduce emotional body gestures as a component of what is commonly known as ”body language” and comment general aspects as gender differences and culture dependence. We then define a complete framework for automatic emotional body gesture recognition. We introduce person detection and comment static and dynamic body pose estimation methods both in RGB and 3D. We then comment the recent literature related to representation learning and emotion recognition from images of emotionally expressive gestures. We also discuss multi-modal approaches that combine speech or face with body gestures for improved emotion recognition. While pre-processing methodologies (e.g., human detection and pose estimation) are nowadays mature technologies fully developed for robust large scale analysis, we show that for emotion recognition the quantity of labelled data is scarce. There is no agreement on clearly defined output spaces and the representations are shallow and largely based on naive geometrical representations.

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Survey on Emotional Body Gesture Recognition

Internet of Things: Novel Advances and Envisioned Applications

Deep Learning Approach for Emotion Recognition from Human Body Movements with Feedforward Deep Convolution Neural Networks

Recognition and study of human emotions have fascinated a lot of attention in the past two decades and have been researched broadly in the field of computer vision. The recognition of complete-body expressions is significantly harder, because the pattern of the human pose has additional degrees of self-determination than the face alone, and its overall shape varies robustly during articulated motion. This chapter presents a method for emotion recognition based on the gesture dynamics features extracted from the foreground object to represent various levels of a person’s posture. The experiments are carried out using publicly available emotion recognition dataset, and the extracted motion feature set is modeled by support vector machines (SVM), Naive Bayes, and dynamic time wrapping (DTW) which are used to classify the human emotions. Experimental results show that DTW is efficient in recognizing the human emotion with an overall recognition accuracy of 93.39 %, when compared to SVM and Naive Bayes.

Automatic Human Emotion Recognition in Surveillance Video

Manual video surveillance is highly expensive and inconvenient in continuous monitoring, by a security personnel. So automatic video surveillance and activity recognition is needed. In this paper, an activity recognition approach is proposed, the difference image is used to extract the motion information based on Region of Interest (ROI). The experiments are carried out on KTH dataset, considering four activities viz (walking, running, waving and boxing) and Weizmann dataset, considering four activities viz (walking, running, waving one hand, waving both hands) with Support Vector Machines (SVM) for classification. This approach shows an overall performance of 94.75% using KTH dataset and 92% using Weizmann dataset to recognize the actions. The performance of the proposed approach is comparable with well known existing methods.

Motion Intensity Code for Action Recognition in Video Using PCA and SVM

Appropriate organization of video databases is essential for pertinent indexing and retrieval of visual information. This paper proposes a new feature called block intensity comparison code (BICC) for video classification and retrieval. Block intensity comparison code represents the average block intensity difference between blocks of a frame. The extracted feature is further processed using principal component analysis (PCA) to reduce the redundancy while exploiting the correlations between the feature elements. The temporal nature of video is modeled by hidden Markov model (HMM) with BICC as the features. It is found that, BICC outperforms other visual features such as edge, motion and histogram which are commonly used for video classification.

M. Kalaiselvi Geetha

Papers

Internet of Things: Novel Advances and Envisioned Applications

Deep Learning Approach for Emotion Recognition from Human Body Movements with Feedforward Deep Convolution Neural Networks

Automatic Human Emotion Recognition in Surveillance Video

Motion Intensity Code for Action Recognition in Video Using PCA and SVM

A novel block intensity comparison code for video classification and retrieval