IEEE Trans Image Process - Gait-based person recognition using arbitrary view transformation model.

Tópicos

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Resumo

Gait recognition is a useful biometric trait for person authentication because it is usable even with low image resolution. One challenge is robustness to a view change (cross-view matching); view transformation models (VTMs) have been proposed to solve this. The VTMs work well if the target views are the same as their discrete training views. However, the gait traits are observed from an arbitrary view in a real situation. Thus, the target views may not coincide with discrete training views, resulting in recognition accuracy degradation. We propose an arbitrary VTM (AVTM) that accurately matches a pair of gait traits from an arbitrary view. To realize an AVTM, we first construct 3D gait volume sequences of training subjects, disjoint from the test subjects in the target scene. We then generate 2D gait silhouette sequences of the training subjects by projecting the 3D gait volume sequences onto the same views as the target views, and train the AVTM with gait features extracted from the 2D sequences. In addition, we extend our AVTM by incorporating a part-dependent view selection scheme (AVTM_PdVS), which divides the gait feature into several parts, and sets part-dependent destination views for transformation. Because appropriate destination views may differ for different body parts, the part-dependent destination view selection can suppress transformation errors, leading to increased recognition accuracy. Experiments using data sets collected in different settings show that the AVTM improves the accuracy of cross-view matching and that the AVTM_PdVS further improves the accuracy in many cases, in particular, verification scenarios.

Resumo Limpo

gait recognit use biometr trait person authent usabl even low imag resolut one challeng robust view chang crossview match view transform model vtms propos solv vtms work well target view discret train view howev gait trait observ arbitrari view real situat thus target view may coincid discret train view result recognit accuraci degrad propos arbitrari vtm avtm accur match pair gait trait arbitrari view realiz avtm first construct d gait volum sequenc train subject disjoint test subject target scene generat d gait silhouett sequenc train subject project d gait volum sequenc onto view target view train avtm gait featur extract d sequenc addit extend avtm incorpor partdepend view select scheme avtmpdv divid gait featur sever part set partdepend destin view transform appropri destin view may differ differ bodi part partdepend destin view select can suppress transform error lead increas recognit accuraci experi use data set collect differ set show avtm improv accuraci crossview match avtmpdv improv accuraci mani case particular verif scenario

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