Face liveness detection through face structure analysis
Liveness detection is a way to detect whether the person is live or not during submission of his/her biometric trait. It is mandatory in order to prevent face spoofing attacks. Therefore, in this paper, we proposed a robust face structure analysis mechanism to detect the liveness by exploiting face shape information. 3D structure/shape of the face is measured on the basis of disparity map between left and right image taken by stereoscopic vision. A gradient–based eight neighbour feature extraction technique has been proposed to extract unique features from these disparity images. It produces minimal computational cost by taking subset of the overall image. We have applied linear discriminant analysis (LDA), C–means algorithms on these features while principal component analysis (PCA) is applied on raw disparity images. We have achieved a recognition rate of 91.6%, 97.5% and 98.3% using PCA, LDA and C–means respectively, which strengthened the confidence of our proposed feature extraction technique.
Face recognition with liveness detection using eye and mouth movement
The recent literature on face recognition technology discusses the issue of face spoofing which can bypass the authentication system by placing a photo/video/mask of the enrolled person in front of the camera. This problem could be minimized by detecting the liveness of the person. Therefore, in this paper, we propose a robust liveness detection scheme based on challenge and response method. The liveness module is added as extra layer of security before the face recognition module. The liveness module utilizes face macro features, especially eye and mouth movements in order to generate random challenges and observing the user's response on account of this. The reliability of liveness module is tested by placing different types of spoofing attacks with various means, like using photograph, videos, etc. In all, five types of attacks have been taken care of and prevented by our system. Experimental results show that system is able to detect the liveness when subjected to all these attacks except the eye & mouth imposter attack. This attack is able to bypass the liveness test but it creates massive changes in face structure. Therefore resultant unrecognized or misclassified by the face recognition module. An experimental test conducted on 65 persons on university of Essex face database confirms that removal of eye and nose components results 75% misclassification.
Sketch drawing by NAO humanoid robot
This paper demonstrates the sketch drawing capability of NAO humanoid robot. Two redundant degrees of freedom elbow yaw (RElbowYaw) and wrist yaw (RWristYaw) of the right hand have been sacrificed because of their less contribution in drawing. The Denavit-Hartenberg (DH) parameters of the system has been defined in order to measure the working envelop of the right hand as well as to achieve the inverse kinematic solution. A linear transformation has been used to transform the image points with respect to real world coordinate system and novel 4 point calibration technique has been proposed to calibrate the real world coordinate system with respect to NAO end effector.
Visual perception-based criminal identification: a query-based approach
The visual perception of eyewitness plays a vital role in criminal identification scenario. It helps law enforcement authorities in searching particular criminal from their previous record. It has been reported that searching a criminal record manually requires too much time to get the accurate result. We have proposed a query-based approach which minimises the computational cost along with the reduction of search space. A symbolic database has been created to perform a stringent analysis on 150 public (Bollywood celebrities and Indian cricketers) and 90 local faces (our data-set). An expert knowledge has been captured to encapsulate every criminal’s anatomical and facial attributes in the form of symbolic representation. A fast query-based searching strategy has been implemented using dynamic decision tree data structure which allows four levels of decomposition to fetch respective criminal records. Two types of case studies - viewed and forensic sketches have been considered to evaluate the strength of our proposed approach. We have derived 1200 views of the entire population by taking into consideration 80 participants as eyewitness. The system demonstrates an accuracy level of 98.6% for test case I and 97.8% for test case II. It has also been reported that experimental results reduce the search space up to 30 most relevant records.
Face recognition using facial symmetry
Face is the most frequently used biometric trait after fingerprint. Its applicability made it popular in different areas such as Human Robot Interaction (HRI), Security Authentication, and Surveillance to name a few. Face recognition concept is based on two major blocks, training and testing. Usually training is done offline while testing is performed in real time scenario. As the size of the database increases, the recognition rate (time taken by system to recognize) increases. The rate of recognition is directly proportional to the size of the database and the dimension of the images. Human faces have the vertical symmetry; hence we utilized this feature and proposed a half way face recognition approach. Experimental verification on both the full faces and the half faces shows that half faces are also sufficient for recognizing the person. For verifying the efficiency of the approach, we have applied PCA (Principal Component Analysis) on both, the full faces and half faces, and have found that in both the cases, accuracy is almost same. But the recognition rate of half faces is just the half of the full faces.
Eye ball TrackingSketch Drawing by Robotic Hand |
Writing by NAO Humanoid RobotWriting by HOAP-2 Humanoid Robot |
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