Video Biometrics: Surveillance 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE1 Road Map 1.Surveillance. 2.Type Of Surveillance. 3.Computer Surveillance. 4.Surveillance Cameras. 5.Biometric Surveillance. 6.Face Recognition From Video. 7.Face Recognition: Four Broad Categories General Steps To Face Recognition General Steps: Face Recognition Problem Definition: Region Of Interest(roi) Face Recognition In Video Surveillance 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE2 Road Map 8.Face Recognition: Four Broad Categories. 9.Faces Captured In Controlled Environment. 10.Faces Captured In Controlled Environment. 11.Faces Captured In Controlled Environment. 12.Challenges Of Face Recognition In Video. 13.Major Areas and Applications. 14.Video Biometrics: Identification and Verification. 15.Multiple Choice Question. 16.References. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE3 SURVEILLANCE Surveillance is the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting. Surveillance is therefore an ambiguous practice, sometimes creating positive effects, at other times negative. It most usually involves observation of individuals or groups by government organizations. There are some exceptions, such as disease surveillance, which monitors the progress of a disease in a community without for that matter directly observing or monitoring individuals). 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE4 Type of Surveillance 1.Computer surveillance 2.Surveillance cameras 3.Social network analysis 4.Biometric surveillance 5.Aerial surveillance 6.Corporate surveillance 7.Global Positioning System 8.Mobile phones 9.Surveillance devices 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE5 Computer surveillance The huge majority of computer surveillance covers the monitoring of data and traffic on the Internet. There is far too much data on the Internet for human investigators to manually search through all of it. So automated Internet surveillance computers sift through the vast amount of intercepted Internet traffic and identify and report to human investigators traffic considered. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE6 Surveillance Cameras Surveillance cameras are video cameras used for the purpose of observing an area. They are often connected to a recording device or IP network, and may be watched by a security guard or law enforcement officer. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE7 Social network analysis Social network analysis (SNA) is the methodical analysis of social networks. Social network analysis views social relationships in terms of network theory, consisting of nodes (representing individual actors within the network) and ties (which represent relationships between the individuals, such as friendship, kinship, organizational position, sexual relationships, etc.) BIOMETRIC SURVEILLANCE Biometric surveillance is any technology that measures and analyze human physical and/or behavioral characteristics for authentication, identification, or screening purposes. Examples of physical characteristics include fingerprints, DNA, and facial patterns. Examples of mostly behavioral characteristics include gait (a person's manner of walking) or voice. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE9 Aerial surveillance Aerial surveillance is the gathering of surveillance, usually visual imagery or video, from an airborne vehiclesuch as an unmanned aerial vehicle, helicopter, or spy plane. Corporate surveillance Corporate surveillance is the monitoring of a person or group's behavior by a corporation. The data collected is most often used for marketing purposes or sold to other corporations, but is also regularly shared with government agencies. Global Positioning System The Global Positioning System (GPS) is a space-based satellite navigation system that provides location and time information in all weather conditions, anywhere on or near the Earth where there is an unobstructed line of sight to four or more GPS satellites. Surveillance devices FACE RECOGNITION FROM VIDEO Face recognition from video is a widespread topic in biometrics research. Many public places usually have surveillance cameras for video capture. These cameras have their significant value for security purpose. It is widely acknowledged that the face recognition have played an important role in surveillance system as it doesnt need the objects cooperation. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE14 Cont The actual advantages of face based identification over other biometrics are uniqueness and acceptance. In this field, accuracy and speed of identification is a main issue. Unfortunately face recognition algorithms suffered from lots of challenges as: Highly dynamic environment like light conditions. Quality of data. wide range of gestures and changes in features with passage of time. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE15 General steps to face recognition General steps to face recognition from video are: 1.Detect the face from video, 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE16 General steps to face recognition 2.Track face. 3.Capture face. 4.Extract features. 5.Perform preprocessing and classify them. The following figure provides a visual overview for above mentioned steps that make the scenario much clear to understand. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE17 General Steps: Face Recognition Face Detection Input: Video Feature Extraction Face Recognition Results Track the face over time (face tracking) 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE18 Problem Definition: Region of Interest(ROI) In video biometrics, region of interest(ROI) play very important roll. Region of interest extracted from video frames matched against facial model of individual of intersect. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE19 Face Recognition in Video Surveillance From :2/2/2016VIDEO BIOMETRICS:SURVEILLANCE20 Face Recognition: Four Broad Categories The Combination of controlled and uncontrolled environment make following four face recognition broad categories: 1.Controlled facial image to controlled facial image. 2.Uncontrolled facial image to controlled facial image. 3. Controlled facial image to uncontrolled facial image. 4.Uncontrolled facial image to uncontrolled facial image. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE21 Cont Controlled environment Images mentions to those which are taken with object cooperation having desired light, shadow, angle and gesture Uncontrolled environment It refers the other way like recognition of object at given distance in insufficient lighting environment, moving object with background. Faces Captured In Controlled Environment Faces captured in controlled environment (as in e-Gates with e-Passport) are much easier to recognize still images captures from these environments may provide: 1.Canonical face model adopted by ICAO02 for passport-type documents. 2.High resolution (60 pixels between eyes) well positioned face (front-faced, eye-level) without occlusion (eye-glasses, scarf neutral facial expression). 3.High quality: no motion, blur, compression artifact. 4.Best possible illumination 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE23 Faces Captured In Controlled Environment 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE24 Faces Captured In Controlled Environment 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE25 Challenges Of Face Recognition In Video Face recognition in video environments are very complex and change over time due to following : 1.Low quality and resolution of video frames. 2.Limited number of acquisition conditions. A.Variation in pose. B.Facial expression. C.Luminance Condition. D.Occlusion problem. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE26 Challenges of face recognition in video Class Based Problem Interclass variability. Intra class variability. Computational resources problem 1.Transmit archive massive quantities of data. 2.Memory requirements: Storage and facial feature retrieval. 3.Processing time. 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE27 Major Areas and Applications BiometricPerson Identification Automated Identity Verification Welfare Fraud Personal SecurityHome Video Surveillance Expression Interpretation Information SecurityUser Authentication Access Security Desktop Login Application Security Database Security Data Privacy 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE28 Major Areas and Applications Law EnforcementCCTV Control Video Surveillance Suspect Identification & Tracking EntertainmentVideo Game Systems Photo Camera Applications 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE29 Video Biometrics: Identification and Verification 2/2/2016VIDEO BIOMETRICS:SURVEILLANCE30 Input: Image/Video Face Detection Face Tracking Pose Estimation Comparison Feature extraction Face recognition Face Feature Tracking. Emotion feature Tracking Gaze estimation Holistic Template Face Geometry based Recognition. Face identification / Verification Simultaneously Cont Face Detection: Face detection is a computer technology that determines the locations and sizes of human faces in arbitrary (digital) images. It detects facial features and ignores anything else, such as buildings, trees and bodies. Face Extraction: When the input data to an algorithm is too large to be processed and it is suspected to be notoriously redundant (e.g. the same measurement in both feet and meters) then the input data will be transformed into a reduced representation set of features (also named features vector). References Faizan Ahmad and Aaima Najam Video-Based Face Classification Approach : ASurvey,IEE Conference on Robotics and Artificial Intelligence(ICRAI),2012, /12. K. C. Lee, J. Ho, M. H. Yang, and D. Kriegman. Video based face recognition using probabilistic appearance manifolds. In Proceedings of International Conference on Computer Vision and Pattern Recognition, W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, Face Recognition: A Literature Survey, ACM Computing Surveys, pp , /2/2016VIDEO BIOMETRICS:SURVEILLANCE32 References2/2/2016VIDEO BIOMETRICS:SURVEILLANCE33