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Seven body parts definition

Seven body parts definition

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Conference Paper
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In part-based human gait recognition, choosing the appropriate body parts is the most challenging problem. The various cofactors such as carrying conditions (backpack or side bag or hand bag), cloths (long coat or jacket or gown) affect various body parts. Here, we proposed a method for detecting the various cofactors in early stage. We have taken...

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Context 1
... divide the human body into seven body parts (Head+Neck, Chest, Abdomen, Pelvis, Thigh, Shank, and Foot) based on anatomical studies of gait [10] given in Fig. 4. When a cofactor is detected then the corresponding combination of body parts are discarded in early stage. The remaining body parts are selected as effective parts and used in final classification. The selected effective body parts and discarded affected body parts for each cf cofactor are given in Table I. III. CLASSIFICATION Each ...

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Gait is a very reliable and efficient bio-metric for human identification. But various cofactors such as carrying condition (backpack or side bag or hand bag), wearing cloth (long coat or jacket or gown) etc. greatly affects a large amount of gait features. In this paper, we proposed a method to detect various types of cofactors for efficient whole...

Citations

... al [8] introduced the difficulties with a different type of cofactors including surfaces, shoes, or viewing angles and presented a baseline algorithm to solve those challenging problem. For cofactored GEI detection and efficient gait recognition, several part based mechanisms were proposed in [9][10][11][12] by dividing human body into eight, seven or three parts respectively. They divide GEI according to the anatomical property [13,14] of the human body and detected cofactors are excluded from these parts as it is considered as a less effective part [10,12] for recognition schemes. ...
Conference Paper
Full-text available
Gait is an important physiological biometric in the area of computer vision for human authentication at a distance. In appearance-based gait recognition system, significant gait features could be affected by various cofactors such as cloths or carrying objects. Therefore, detecting co-factored segments and eliminating co-factored information without losing the features of Gait Energy Image (GEI) is one of the major concerns for appropriate gait recognition. In this paper, we proposed a method for detecting cofactor affected segments of GEI and an approach for dynamic reconstruction of co-factored GEI for more accurate gait recognition. The whole GEI is first segmented into three parts considering the area of cofactor appearance in it. Moreover, co-factored information are detected and eliminated depending on some predefined threshold values. Finally, the three segments are recombined for final classification. The CASIA gait database is used here as a training and a test data. The result shows better performance with 85.04% accuracy which is more convenient than other conventional gait recognition methods.