Abstract
Person Recognition based on Gait Model (PRGM) and motion features is are indeed a challenging and novel task due to their usages and to the critical issues of human pose variation, human body occlusion, camera view variation, etc. In this project, a deep convolution neural network (CNN) was modified and adapted for person recognition with Image Augmentation (IA) technique depending on gait features. Adaptation aims to get best values for CNN parameters to get best CNN model. In Addition to the CNN parameters Adaptation, the design of CNN model itself was adapted to get best model structure; Adaptation in the design was affected the type, the number of layers in CNN and normalization between them. After choosing best parameters and best design, Image augmentation was used to increase the size of train dataset with many copies of the image to boost the number of different images that will be used to train Deep learning algorithms. The tests were achieved using known dataset (Market dataset). The dataset contains sequential pictures of people in different gait status. The image in CNN model as matrix is extracted to many images or matrices by the convolution, so dataset size may be bigger by hundred times to make the problem a big data issue. In this project, results show that adaptation has improved the accuracy of person recognition using gait model comparing to model without adaptation. In addition, dataset contains images of person carrying things. IA technique improved the model to be robust to some variations such as image dimensions (quality and resolution), rotations and carried things by persons. Results for 200 persons recognition, validation accuracy was about 82% without IA and 96.23 with IA. For 800 persons recognition, validation accuracy was 93.62% without IA.
Highlights
Gait is a kind of behavioral biometric feature, it is defined as the way a person moves and the movement of every person has no typical form or scenarios.There are other behavioral biometrics like face and iris but they are limited by the distance between the person and the used camera
Convolution Neural network model is an important type of feed-forward neural network with special success on applications where the target information can be represented by a hierarchy of local features [21]
Conclusion and future scope In summary, This work proposed simple and robust model for person recognition using gait model features based on convolution neural network (CNN) algorithm, this model resulted with edits in the design of CNN model and choosing hyper parameters for some parts of CNN model
Summary
Gait is a kind of behavioral biometric feature, it is defined as the way a person moves and the movement of every person has no typical form or scenarios. After mentioning many application for PRGM and with the increasing demand for person recognition in the era of big data and artificial intelligence, the research and development of many algorithms attracted broad attention from both academia and industry, e.g., the fingerprint, iris, face, and voice etc., have been implemented commercially All examples in this field will output huge amount of data ( 1,2 or dimensions), we need a Convolution neural network to handle features that can be concluded from the input data. The majority of previous approaches to gait recognition have used subspace learning methods which have several shortcomings that we avoid Their specialized deep CNN model can obtain competitive performance when tested on the CASIA-B large gait data set; CASIA data set has only 20 persons. Augmentation will be counterproductive if it produces images very dissimilar to what the model will be tested on, so this process must be executed with care [20]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
