Video Biometrics (original) (raw)

Speed Invariant, Human Gait Based Recognition System for Video Surveillance Security

Human gait provides an important and useful behavioral biometric signature which characterizes the nature of an individual's walking pattern. This inherent knowledge of gait feature confirms the correct identification of a person in a video surveillance footage scenario. In this paper, we attempt to use computer vision based technique to derive the gait signature of a person which is a major criterion for the gait based recognition system. The gait signature has been obtained from the sequence of silhouette images at various gait speeds varying from 2km/hr. to 7km/hr. The OU-ISIR Treadmill walking speed databases have been used in our research work. The joint angles of knee and ankle are computed from the stick figure of corresponding human silhouettes which lead to construct our feature template together with the other gait attributes such as width, height, area and diagonal angle of human silhouette. The combined gait features will make the system robust in different gait speeds. The major concept behind making the gait recognition speed invariant is that the human can walk in finite speed so instead of training the classifier for a single speed the classifier is to be trained for multiple speeds. A minimum distance classifier is used to separate out different cluster of subject with combined feature vectors at different gait speeds.

Human Identification Based on Gait Video Sequences

The authors present results of the research on human recognition based on the video gait sequences from the CASIA Gait Database. Both linear (principal component analysis; PCA) and nonlinear (locally linear embedding; LLE) methods were applied in order to reduce data dimensionality, whereas a concept of hidden Markov model (HMM) was used for the purpose of data classification. The results of the conducted experiments formed the main subject of analysis of classification accuracy expressed by means of the Correct Classification Rate (CCR).

Fusion of gait and face for human identification

2004

Identification of humans from arbitrary view points is an important requirement for different tasks including perceptual interfaces for intelligent environments, covert security and access control etc. For optimal performance, the system must use as many cues as possible and combine them in meaningful ways. In this paper we present fusion of face and gait cues for the single camera case. We employ a view invariant gait recognition algorithm for gait recognition. A sequential importance sampling based algorithm is used for probabilistic face recognition from video. We employ decision fusion to combine the results of our gait recognition algorithm and the face recognition algorithm. We consider two fusion scenarios: hierarchical and holistic. The first involves using the gait recognition algorithm as a filter to pass on a smaller set of candidates to the face recognition algorithm. The second involves combining the similarity scores obtained individually from the face and gait recognition algorithms Simple rules like the SUM, MIN and PRODUCT are used for combinining the scores. The results of the fusion are demonstrated on the NIST database which has outdoor gait and face data of 30 subjects.

Integrating Face and Gait for Human Recognition at a Distance in Video

IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), 2007

This paper introduces a new video-based recognition method to recognize noncooperating individuals at a distance in video who expose side views to the camera. Information from two biometrics sources, side face and gait, is utilized and integrated for recognition. For side face, an enhanced side-face image (ESFI), a higher resolution image compared with the image directly obtained from a single video frame, is constructed, which integrates face information from multiple video frames. For gait, the gait energy image (GEI), a spatio-temporal compact representation of gait in video, is used to characterize human-walking properties. The features of face and gait are obtained separately using the principal component analysis and multiple discriminant analysis combined method from ESFI and GEI, respectively. They are then integrated at the match score level by using different fusion strategies. The approach is tested on a database of video sequences, corresponding to 45 people, which are collected over seven months. The different fusion methods are compared and analyzed. The experimental results show that: 1) the idea of constructing ESFI from multiple frames is promising for human recognition in video, and better face features are extracted from ESFI compared to those from the original side-face images (OSFIs); 2) the synchronization of face and gait is not necessary for face template ESFI and gait template GEI; the synthetic match scores combine information from them; and 3) an integrated information from side face and gait is effective for human recognition in video.

Robust Analytics for Video-Based Gait Biometrics

2017

Gait analysis is the study of the systematic methods that assess and quantify animal locomotion. The research on gait analysis has considerably evolved through time. It was an ancient art, and it still finds its application today in modern science and medicine. Gait finds a unique importance among the many state-of-the-art biometric systems since it does not require the subject's cooperation to the extent required by other modalities. Hence by nature, it is an unobtrusive biometric. Gait is associated with three types of signals, viz., kinetic, kinematic and EMG. Kinesiological EMG is mostly used for clinical purposes and kinetic measurement instruments are confined to a limited space. The applicability of kinetic and EMG data for the purpose of biometrics is thus constrained. Kinematic observation of gait is much more efficient and successful in literature. Also, the cost of kinematic measurement instruments such as a video camera and mobile accelerometer are much more cost effective than the apparatus required for EMG and kinetic observation. There are two aspects to biometrics: hard and soft. Soft biometrics include determination of height, weight, gender, or ethnicity with gender recognition being the most common form associated with gait research. The method proposed in this thesis, Pose-Based Voting employs a scheme which delineates the gait instance as a sequence of poses which is used to predict the gender of the respective subject through a voting scheme. Hard biometrics associate people with their innate traits that identifies them. This data can be used either for identification or for authentication. Gait identification, widely known as gait recognition, is the process of mapping a given gait instance to a trained identity. On the other hand, gait authentication shows how v ACKNOWLEDGEMENT I wish to record my deep sense of gratitude and profound thanks to my research supervisor Dr. K. S. Easwarakumar for his well-organized approach and inspiring guidance which helped me bring this thesis to fruition. I also thank

A Comprehensive Review of Past and Present Vision-based Techniques for Gait Recognition

Springer MTA, 2013

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available—for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.

Vision-based techniques for gait recognition

ArXiv, 2020

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance.

Gait-based person authentication by wearable cameras

2012 Ninth International Conference on Networked Sensing (INSS), 2012

In this paper, we propose a novel gait-based person authentication by wearable cameras, and quantitatively evaluate its authentication accuracy. In contrast to previous methods using motion sensors, we utilize wearable cameras for personal authentication by examining motion during walking obtained from visual information. This motion appears to provide a valid representation of the gait of the wearer. We then developed a prototype system of wearable surveillance, which is a new concept of surveillance we have proposed. The performance of our gaitbased person authentication method was experimentally evaluated with 39 subjects using the prototype system, revealing an Equal Error Rate (EER) of 5.6%.