scholarly journals Measuring Farm Animal Emotions—Sensor-Based Approaches

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 553
Author(s):  
Suresh Neethirajan ◽  
Inonge Reimert ◽  
Bas Kemp
Keyword(s):  
Emotional Expression ◽  
Emotional Responses ◽  
Facial Feature ◽  
Agricultural Technology ◽  
Farm Animals ◽  
Expression Recognition ◽  
Biometric Data ◽  
Challenges And Opportunities ◽  
Expression Of Emotions ◽  
Expression Measurement

Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no ‘benchmarks’ or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal’s emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals’ compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities.

A Review of Facial Feature Detection Algorithms

2010 ◽  
pp. 42-61
Author(s):  
Stylianos Asteriadis ◽  
Stylianos Asteriadis ◽  
Nikos Nikolaidis ◽  
Nikos Nikolaidis ◽  
Ioannis Pitas ◽  
...  
Keyword(s):  
Feature Detection ◽  
Performance Metrics ◽  
Facial Feature ◽  
Research Field ◽  
Head Pose Estimation ◽  
Expression Recognition ◽  
Facial Feature Detection ◽  
Detection Algorithms ◽  
Active Research ◽  
And Performance

Facial feature localization is an important task in numerous applications of face image analysis that include face recognition and verification, facial expression recognition, driver‘s alertness estimation, head pose estimation etc. Thus, the area has been a very active research field for many years and a multitude of methods appear in the literature. Depending on the targeted application, the proposed methods have different characteristics and are designed to perform in different setups. Thus, a method of general applicability seems to be away from the current state of the art. This chapter intends to offer an up-to-date literature review of facial feature detection algorithms. A review of the image databases and performance metrics that are used to benchmark these algorithms is also provided.

A Robust Facial Feature Tracking Method Based on Optical Flow and Prior Measurement

2010 ◽  
Vol 4 (4) ◽  
pp. 62-75
Author(s):  
Guoyin Wang ◽  
Yong Yang ◽  
Kun He
Keyword(s):  
Optical Flow ◽  
Feature Tracking ◽  
Facial Feature ◽  
Recognition System ◽  
Research Area ◽  
Expression Recognition ◽  
Tracking Method ◽  
Facial Feature Tracking ◽  
Recognition Result ◽  
Method Of Measurement

Cognitive informatics (CI) is a research area including some interdisciplinary topics. Visual tracking is not only an important topic in CI, but also a hot topic in computer vision and facial expression recognition. In this paper, a novel and robust facial feature tracking method is proposed, in which Kanade-Lucas-Tomasi (KLT) optical flow is taken as basis. The prior method of measurement consisting of pupils detecting features restriction and errors and is used to improve the predictions. Simulation experiment results show that the proposed method is superior to the traditional optical flow tracking. Furthermore, the proposed method is used in a real time emotion recognition system and good recognition result is achieved.

Facial Expression Recognition

2011 ◽  
pp. 255-317 ◽  
Author(s):  
Daijin Kim ◽  
Jaewon Sung
Keyword(s):  
Facial Expression ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Muscle Group ◽  
Expression Recognition ◽  
Coding System ◽  
Facial Action Coding System ◽  
Multiple Cultures ◽  
Expression Of Emotions ◽  
Objective Description

The facial expression has long been an interest for psychology, since Darwin published The expression of Emotions in Man and Animals (Darwin, C., 1899). Psychologists have studied to reveal the role and mechanism of the facial expression. One of the great discoveries of Darwin is that there exist prototypical facial expressions across multiple cultures on the earth, which provided the theoretical backgrounds for the vision researchers who tried to classify categories of the prototypical facial expressions from images. The representative 6 facial expressions are afraid, happy, sad, surprised, angry, and disgust (Mase, 1991; Yacoob and Davis, 1994). On the other hand, real facial expressions that we frequently meet in daily life consist of lots of distinct signals, which are subtly different. Further research on facial expressions required an object method to describe and measure the distinct activity of facial muscles. The facial action coding system (FACS), proposed by Hager and Ekman (1978), defines 46 distinct action units (AUs), each of which explains the activity of each distinct muscle or muscle group. The development of the objective description method also affected the vision researchers, who tried to detect the emergence of each AU (Tian et. al., 2001).

Research on the Facial Expression Feature Extraction of Facial Expression Recognition Based on MATLAB

2014 ◽  
Vol 1049-1050 ◽  
pp. 1522-1525
Author(s):  
Wang Ju ◽  
Ding Rui ◽  
Chun Yan Nie
Keyword(s):  
Pattern Recognition ◽  
Feature Extraction ◽  
Facial Expression ◽  
Interpersonal Communication ◽  
Facial Expressions ◽  
Facial Expression Recognition ◽  
Facial Feature ◽  
Expression Recognition ◽  
Information Communication ◽  
Facial Feature Extraction

In such a developed day of information communication, communication is an important essential way of interpersonal communication. As a carrier of information, expression is rich in human behavior information. Facial expression recognition is a combination of many fields, but also a new topic in the field of pattern recognition. This paper mainly studied the facial feature extraction based on MATLAB, by MATLAB software, extracting the expression features through a large number of facial expressions, which can be divided into different facial expressions more accurate classification .

Physicality’s Sociality and Sociality’s Physicality

2017 ◽  
Author(s):  
Maria Tapias
Keyword(s):  
Emotional Expression ◽  
The Body ◽  
The Andes ◽  
Expression Of Emotions ◽  
The Individual ◽  
The Relationship ◽  
Porous Boundaries

This chapter explores the elusive and porous boundaries of the body among Punata residents as well as the relationship between physicality and sociality. It also considers the conceptualizations people have about the “fluidity” of emotions and the predicaments inherent in the expression of emotions, larger Andean notions of corporeality, sociability, and the flow of substances between individuals. It shows that the individual and social bodies are intrinsically interconnected throughout the Andes: action in one physical or emotional sphere can exert effects in another. It explains how the boundaries of the body are deeply challenged in the context of Punata and offer insights into how illness processes are conceptualized and lived. The chapter concludes with a discussion of the politics of emotional expression; how the “mechanics” of the accumulation and elimination of emotions are inextricably linked to gender, class, ethnicity, and age.

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