Person Identification Using Deep Learning
|Typ||Master, Forschungspraxis, IDP|
|Betreuer||Maryam Babaee, M.Sc.
Tel.: +49 (0)89 289-28543
|Beschreibung||As one of the biometric features for human identification, Gait (the way of walking) has drawn attention in recent years, since it is able to recognize people from a large distance in spite of other biometric features such as face, fingerprint. In gait recognition, a sequence of images showing a person walking are analyzed as input data .
The performance of gait recognition can be adversely affected by many sources of variations such as viewing angle. In real scenarios, people might walk in different directions toward the camera, which makes the gait recognition more challenging. Therefore, learning view-invariant gait representation is highly desirable. The gait images captured from different view angles can be transformed into their corresponding side view images, which contain more dynamic information.
Recently, Generative Adversarial Networks (GAN)  and its variants  have been successfully applied for video and image generation. In this work, we aim to deploy such neural network for our desired human identification based on Gait cues observed from multiple viewing angels .
|Voraussetzung||Preliminary knowledge in Machine learning and deep learning as well as good programming skill in Python and Tensorflow are highly required.|
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