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 Development of Human Identification Systems for Visual Surveillance in Security-Sensitive Environments

General objectives

  1. Develop human identification technologies for surveillance and security systems, which combine advantages of biometrics (face recognition, gait recognition) and human motion analysis (motion analysis of human body parts, human motion tracking) to recognize a person in a crowd or singularly.
  2. Design surveillance systems to identify individuals at a distance in security-sensitive environments such as banks and airports.

Specific objectives

  1. Develop and evaluate algorithms for face recognition over image sequences, gait recognition, human movement tracking, and human pose and motion analysis.
  2. Synchronize the 4 different stages of human identification procedure, i.e., face recognition, gait recognition, human movement tracking, and human pose and motion analysis.
  3. Design a hierarchical decision-making scheme for identity recognition based on the results of the 4 different stages.

Main Activities:

  1. Face recognition over image sequences: Develop face recognition algorithms using the advantage of additional information obtained in image sequences, which can be used for either tracking persons over image sequences or identify a person. The developed algorithms can also estimate head pose, remove sunglasses and scarf, and recognize facial expressions.
  2. Gait recognition: Develop gait recognition algorithms based on silhouette analysis from videos, which aims to identify individuals by the way they walk. These algorithms should produce stable and accurate results in the presence of variations such as shoe-wear type, weight carried, camera viewpoint, and data collecting time.
  3. Human movement tracking: Develop model-based tracking algorithms for moving humans by focusing on the motion of the whole body, which can be used as a guide for predicting and estimating the movement of body parts (such as head, neck, shoulders, elbows, wrists, torso, hips, knees, ankles). The human movement tracking algorithms should segment rapidly changing scenes in natural environments involving non-rigid motion and occlusion, and track humans either from a single view or from multiple perspectives.
  4. Human body pose and motion analysis: Qualitatively and quantitatively analyze human body pose and motion by using high level reasoning and kinematic modeling, respectively. High level reasoning can discriminate the relative position and motion of body parts without requiring the precise location of joints or edges. Kinematic modeling can detect, track and identify articulated objects (humans) by modeling the underlying dynamics of human locomotion and biomechanics. 2D and 3D methods will be designed for interpretation of human body structure by focusing on motion estimation of the joints of body parts.
  5. Synchronization of the 4 different stages: Design a real-time dynamic framework to evaluate which stages will work well in the current frame streaming, to conduct the selected stages and to produce the estimation results for the current frame streaming.
  6. Hierarchical decision-making scheme for identity recognition: Design a dynamic multilayered identity estimation method based on the estimation results from the different stages and from the different frame streaming.
  7. Database collection: Build up a number of databases to aid in the evaluation of algorithms for face recognition, gait recognition, human movement tracking, and human pose and motion analysis.


The computer system will be able to convert the pixels from the camera into data sequences that would then be assigned to the parts of the body that would have specific fields assigned , such as head, neck, chest, arms etc. The pattern for each area would be further broken down on an in-depth ( micro ) mapping system, then converted again to pick out detail ( micro ) movements of the joint, muscle, skin, the system would have to be able to virtually remove clothing and accessories and (peer) beneath. This could be accomplished with a form of ( x ray, laser ) scanning. For the face , should it be covered by e.g.: sunglasses, facial hair, or plastic surgery ( which would not necessarily change the shape or size of the skeletal form) .

To match a body profile in 3D using software that will allow downloading a moving picture of the subject and divide the entire body using the body's joints as markers focusing on the movement of that particular part in depth using a grid method. With the transfer of all the points and movements from the grid to an overall movement structure, this will create a format for this individual which would be unique.