Projects:2018s1-100 Automated Person Identification with Multiple Sensors
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.
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
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.
Gait Analysis
Results
To be completed
Conclusion
To be completed
