Gait recognition has received increasing attention for security and authentication since it can be done unintrusively from afar and without a subject's awareness. In this work, we propose a new model-based gait recognition technique called JRC-CNN gait recognition. We introduce three new concepts. (1) We create a new way to preprocess skeleton data by rotating skeleton data using two virtual axes. This process reduces the fluctuation in movements and resolves the multi-viewpoint issue. All postures in a walk are observed from the same angle. (2) We introduce new Joint Replacement Coordinates (JRCs), which represent the movements of the left and right joints in a group of three connected joints. These JRC gait features are designed to put more emphasis on local movements than the movements of non-connected joints. (3) We construct a new Convolution Neural Network (CNN) for the classification process, which consists of a convolutional layer on each JRC and two fully-connected layers. A convolutional layer is designed to discover relations within a group of three connected joints. Fully-connected layers also find the relations of all groups of three connected joints throughout an entire body (in a posture). Our JRC-CNN technique achieves above 98.4% accuracy and significantly outperforms other existing techniques for all free-direction walk datasets. It also performs well under the gallery-size test and the CMC curve test. This means that our proposed JRC-CNN gait recognition technique can be used in a real-world situation. Experimental results also suggest that a person can be identified by a unique posture (an entire body is observed as a whole) with the focus on the movements of connected joints.

With the increase of terrorist threats around the world, human identification research has become a sought after area of research. Unlike standard biometric recognition techniques, gait recognition is a non-intrusive technique. Both data collection and classification processes can be done without a subject's cooperation. In this paper, we propose a new model-based gait recognition technique called postured-based gait recognition. It consists of two elements: posture-based features and posture-based classification. Posture-based features are composed of displacements of all joints between current and adjacent frames and center-of-body (CoB) relative coordinates of all joints, where the coordinates of each joint come from its relative position to four joints: hip-center, hip-left, hip-right, and spine joints, from the front forward. The CoB relative coordinate system is a critical part to handle the different observation angle issue. In posture-based classification, postured-based gait features of all frames are considered. The dominant subject becomes a classification result. The postured-based gait recognition technique outperforms the existing techniques in both fixed direction and freestyle walk scenarios, where turning around and changing directions are involved. This suggests that a set of postures and quick movements are sufficient to identify a person. The proposed technique also performs well under the gallery-size test and the cumulative match characteristic test, which implies that the postured-based gait recognition technique is not gallery-size sensitive and is a good potential tool for forensic and surveillance use.

In this work, we develop a methodology for determining the communication required to implement various two-party functionalities noninteractively. In the particular setting on which we focus, the protocols are based upon somewhat homomorphic encryption, and furthermore, they treat the homomorphic properties as a black box. In this setting, we develop lower bounds which give a smooth trade-off between the communication complexity and the “expressiveness” of the cryptosystem---the latter being measured in terms of the depth of the arithmetic circuits that can be evaluated on ciphertext. Given the current state of the art in homomorphic encryption, this trade-off may also be viewed as one between communication and computation, since at present, more expressive cryptosystems are markedly less efficient. We then apply this methodology to place lower bounds on a number of cryptographic protocols including private information retrieval writing and private keyword search. Our work provides a useful “litmus test” of feasibility for use by other cryptographic researchers attempting to develop new protocols that use somewhat homomorphic encryption in a black-box way and require certain levels of communication efficiency. We also answer an open question from the thesis of Doerte K. Rappe [Homomorphic Cryptosystems and Their Applications, Universität Dortmund, Germany, 2006] regarding the construction of fully homomorphic encryption from group homomorphic encryption.