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.
Introduction
Automated person identification is a vital capability for modern security systems. Normal security environments feature uncooperative subjects so robust methods are required. The aims of this project are:
- Create an automated person identification system using 3D data
- Use realistic data that is extendable to real-world applications
- Fuse different methods to improve reliability of identification
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
The results show that the best method was gait analysis, which performed better than anthropometrics. Body features consistently performed better than methods which used facial data, of which local feature extraction performed better than Eigenfaces. Despite the large difference in identification accuracy between each method, fusion improved the overall performance of the system.
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
The aims of the project were achieved, as shown by the following key findings:
- 3D data can be used to create an automated person identification system
- Realistic data can be used to accurately identify people
- Fusion of different methods improves performance
