Determination of Age and Gender Based on Features of Human Motion Using AdaBoost Algorithms

Santoso, Handri; Nomura, Shusaku; Nakamura, Kazuo

doi:10.1007/s12369-010-0089-0

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…Oskuie and Faez [67] extracted Zernike moments from Radon-transformed Mean Gait Energy Image [68]. Frequency-domain features obtained from the silhouette using Discrete Fourier Transform (DFT) [47] and wavelet decomposition on the silhouette contour width [69] have been used. Instead of extracting the silhouette, DCT coefficients was obtained from the image to train embedded hidden Markov models [70].…”

Section: Feature Extractionmentioning

confidence: 99%

Recognizing Human Gender in Computer Vision: A Survey

Tay

Goi

2012

Lecture Notes in Computer Science

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Gender is an important demographic attribute of people. This paper provides a survey of human gender recognition in computer vision. A review of approaches exploiting information from face and whole body (either from a still image or gait sequence) is presented. We highlight the challenges faced and survey the representative methods of these approaches. Based on the results, good performance have been achieved for datasets captured under controlled environments, but there is still much work that can be done to improve the robustness of gender recognition under real-life environments.

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Section: Feature Extractionmentioning

confidence: 99%

Recognizing Human Gender in Computer Vision: A Survey

Tay

Goi

2012

Lecture Notes in Computer Science

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“…Several studies provide valuable information, particularly for human psychology understanding and for human-machine interaction applications. Literature on the ability of human to recognize individuals from motion is abundant, in particular, it has been shown that one can recognize a known person or even him/herself accurately from gait data: arm swing, back posture for example are key features [6][7][8][9][10][11][12][13]. Most of the gait studies make use of parameters such as the stance phase, the gait cycle frequency, the length of the footsteps that can be easily measured [14,15].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Emotions Conveyed by Human Gait

Venture

Kadone

Zhang

et al. 2014

Int J of Soc Robotics

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Humans convey emotions through different ways. Gait is one of them. Here we propose to use gait data to highlight features that characterize emotions. Gait analysis study usually focuses on stance phase, frequency, footstep length.Here the study is based on the joint angles obtained from inverse kinematics computation from the 3D motion-capture data using a combination of degrees of freedom (DOF) out of a 34DOF human body model obtained from inverse kinematics of markers 3D position. The candidates are four professional actors, and five emotional states are simulated: Neutral, Joy, Anger, Sadness, and Fear. The paper presents first a psychological approach which results are used to propose numerical approaches. The first study provides psychological results on motion perception and the possibility of emotion recognition from gait by 32 observers. Then, the motion data is studied using a feature vector approach to verify the numerical identifiability of the emotions. Finally each motion is tested against a database of reference motions to identify the conveyed emotion. Using the first and second study results, we utilize a 6DOF model then a 12DOF model. The experimental results show that by using the gait characteristics it is possible to characterize each emotion with good accuracy for intra-subject data-base. For inter-subject database results show that recognition is more prone to error, suggesting strong inter-personal differences in emotional features.

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“…where I(i,j,t) is a binary silhouette image at current frame t, and I(i,j,t-1) is a binary silhouette image at previous frame t. The model free preprocessing used in this paper by using the motion parameter per frame [22]. First, we have to get the silhouettes image.…”

Section: Free Model Basedmentioning

confidence: 99%

Human Gait Gender Classification in Spatial and Temporal Reasoning

Arai¹,

Asmara²

2012

IJARAI

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Abstract-Biometrics technology already becomes one of many application needs for identification. Every organ in the human body might be used as an identification unit because they tend to be unique characteristics. Many researchers had their focus on human organ biometrics physical characteristics such as fingerprint, human face, palm print, eye iris, DNA, and even behavioral characteristics such as a way of talk, voice and gait walking. Human Gait as the recognition object is the famous biometrics system recently. One of the important advantage in this recognition compare to other is it does not require observed subject's attention and assistance. This paper proposed Gender classification using Human Gait video data. There are many human gait datasets created within the last 10 years. Some databases that widely used are University of South Florida (USF) Gait Dataset, Chinese Academy of Sciences (CASIA) Gait Dataset, and Southampton University (SOTON) Gait Dataset. This paper classifies human gender in Spatial Temporal reasoning using CASIA Gait Database. Using Support Vector Machine as a Classifier, the classification result is 97.63% accuracy.

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Determination of Age and Gender Based on Features of Human Motion Using AdaBoost Algorithms

Cited by 10 publications

References 20 publications

Recognizing Human Gender in Computer Vision: A Survey

Recognizing Human Gender in Computer Vision: A Survey

Recognizing Emotions Conveyed by Human Gait

Human Gait Gender Classification in Spatial and Temporal Reasoning

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