Projects:2018s1-100 Automated Person Identification with Multiple Sensors

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Project Team

Students

Maxwell Standen

Archana Vadakattu

Michael Vincent

Supervisors

Dr Brian Ng

Dr David Booth (DST Group)

Sau-Yee Yiu (DST Group)

Abstract

This project seeks to develop the capability to identify individuals based on their whole body shape and 3D facial characteristics. This will involve:

  • 1. Determining the most suitable sensor type(s) for the task.
  • 2. Data collection, using the most appropriate sensor(s) selected in (1).
  • 3. Data alignment, and extraction of 3D face and body shape features.
  • 4. The development of a recognition and fusion capability using the extracted 3D face and body shape features.
  • 5. Performance assessment.

The project will involve elements of literature survey (both sensor hardware and algorithmic techniques), software development (ideally in Matlab) and performance assessment, possibly by means of a small trial. This project is sponsored by DST Group.

Methodology

  • 1. Acquire data
  • 2. Preprocess data to improve quality
  • 3. Extract features from preprocessed data
  • 4. Use extracted features to create a classifier

Data Acquisition

Data is acquired using the Microsoft Kinect. RGB, depth and skeletal data is captured.

Preprocessing

Preprocessing is used to enhance the quality of the captured data and the facilitate more accurate extraction of features present in the data.

Gait Sequencing

Gait sequencing involves separating data into gait cycles. This provides frame sequences which are suitable for extracting the features used in the project.

Pose Correction

Pose correction is used to realign a face.

Facial Feature Extraction

Facial features are extracted from the depth data.

Local Feature Extraction

Local feature extraction analyses local patterns and textures to determine the similarity between images.

Eigenfaces

Eigenfaces is a facial recognition method which captures the variation in a collection of face images and uses the information to compare faces.

Body Feature Extraction

Body features are extracted from the skeletal data that is captured by the Kinect.

Anthropometrics

Anthropometrics is the analysis of body measurements and is used to identify subjects. This is done by calculating body part lengths based on the positions of skeletal joints relative to each other.

Gait Analysis

Gait analysis involves extracting features related to the movement of individuals. Skeletal joints are tracked as a person walks and are used to calculate things such as stride length and degree of arm swing. These tend to be quite individualistic and so are useful for classification.

Fusion

Combine the face and body recognition results in a structured manner to improve robustness of the person identification system.

Results

Body Features

Anthropometrics

Preliminary testing with a dataset consisting of three subjects showed a 98% classification rate. This was done by inputting body part lengths into a KNN classifier with k = 1.

Conclusion

To be completed

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