Action Recognition Using Single-Pixel Time-of-Flight Detection

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Automatic emotion recognition has become an important trend in many artificial intelligence (AI) based applications and has been widely explored in recent years. Most research in the area of automated emotion recognition is based on facial expressions or speech signals. Although the influence of the emotional state on body movements is undeniable, this source of expression is still underestimated in automatic analysis. In this paper, we propose a novel method to recognise seven basic emotional states—namely, happy, sad, surprise, fear, anger, disgust and neutral—utilising body movement. We analyse motion capture data under seven basic emotional states recorded by professional actor/actresses using Microsoft Kinect v2 sensor. We propose a new representation of affective movements, based on sequences of body joints. The proposed algorithm creates a sequential model of affective movement based on low level features inferred from the spacial location and the orientation of joints within the tracked skeleton. In the experimental results, different deep neural networks were employed and compared to recognise the emotional state of the acquired motion sequences. The experimental results conducted in this work show the feasibility of automatic emotion recognition from sequences of body gestures, which can serve as an additional source of information in multimodal emotion recognition.

Section: Introductionmentioning

confidence: 99%

Emotion Recognition from Skeletal Movements

Sapiński

Kamińska

Pelikant

et al. 2019

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“…Proper identification of emotional state can significantly improve quality of human-computer interfaces. It can be applied for monitoring of psycho-physiological states of individuals e.g., to assess the level of stress or fatigue, forensic data analysis [ 2 ], advertisement [ 3 ], social robotic [ 4 ], video conferencing [ 5 ], violence detection [ 6 ], animation or synthesis of life-like agents xue2018voice, and many others. Automatic emotion recognition methods utilize various input types i.e., facial expressions [ 7 , 8 , 9 ], speech [ 10 , 11 , 12 ], gesture and body language [ 13 , 14 ], physical signals such as electrocardiogram (ECG), electromyography (EMG), electrodermal activity, skin temperature, galvanic resistance, blood volume pulse (BVP), and respiration [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Emotional Speech Recognition Based on the Committee of Classifiers

Kamińska

2019

This article presents the novel method for emotion recognition from speech based on committee of classifiers. Different classification methods were juxtaposed in order to compare several alternative approaches for final voting. The research is conducted on three different types of Polish emotional speech: acted out with the same content, acted out with different content, and spontaneous. A pool of descriptors, commonly utilized for emotional speech recognition, expanded with sets of various perceptual coefficients, is used as input features. This research shows that presented approach improve the performance with respect to a single classifier.

“…In care homes with elderly patients, for example, interaction of the user with typical device-dependent hardware or following specific instruction during biometric scan (e.g., direct contact with a camera, placing a biometric into a specific position, etc.) [ 7 , 8 ]. In other words, the nature of such uncontrolled environments suggest the biometric designer to consider strictly natural and transparent systems that mitigate the user non-cooperativeness behavior, providing an enhanced performance.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Constrained Biometric System for Non-Cooperative Users

Jahromi

Buch-Cardona

Avots

et al. 2019

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With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject's hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.