Comparison of Computer-Based and Optical Face Recognition Paradigms

The main objectives of this thesis are to validate an improved principal components analysis (IPCA) algorithm on images; designing and simulating a digital model for image compression, face recognition and image detection by using a principal components analysis (PCA) algorithm and the IPCA algorithm; designing and simulating an optical model for face recognition and object detection by using the joint transform correlator (JTC); establishing detection and recognition thresholds for each model; comparing between the performance of the PCA algorithm and the performance of the IPCA algorithm in compression, recognition and, detection; and comparing between the performance of the digital model and the performance of the optical model in recognition and detection. The MATLAB © software was used for simulating the models. PCA is a technique used for identifying patterns in data and representing the data in order to highlight any similarities or differences. The identification of patterns in data of high dimensions (more than three dimensions) is too difficult because the graphical representation of data is impossible. Therefore, PCA is a powerful method for analyzing data. IPCA is another statistical tool for identifying patterns in data. It uses information theory for improving PCA. The joint transform correlator (JTC) is an optical correlator used for synthesizing a frequency plane filter for coherent optical systems. The IPCA algorithm, in general, behaves better than the PCA algorithm in the most of the applications. It is better than the PCA algorithm in image compression because it obtains higher compression, more accurate reconstruction, and faster processing speed with acceptable errors; in addition, it is better than the PCA algorithm in real-time image detection due to the fact that it achieves the smallest error rate as well as remarkable speed. On the other hand, the PCA algorithm performs better than the IPCA algorithm in face recognition because it offers an acceptable error rate, easy calculation, and a reasonable speed. Finally, in detection and recognition, the performance of the digital model is better than the performance of the optical model.

Optical Access Engine Setup and Validation

The optical access engine integrated with the diagnostic and optical measurement techniques is a great platform for engine research because it provides clear visual access to the combustion chamber inside the engines. An optical access engine customized based on a 4-cylinder spark ignited direct injection (SIDI) production engine is located in the Advanced Power Systems Laboratories (APS LABS) at Michigan Technological University. This optical access engine inside the test cell has been set up for different engine research. In this report, two SAE papers in engine research utilizing the optical access engine are reviewed to gain basic understanding of the methodology. Though the optical engine in APS LABS is a little bit different from the engines used in the literature, the methodology in the papers provides guidelines for engine research through optical access engines. In addition, the optical access engine instrumentation including the test cell setup and the optical engine setup is described in detail in the report providing a solid record for later troubleshooting and reference. Finally, the motoring tests, firing tests and optical imaging experiment on the optical engine have been performed to validate the instrumentation. This report only describes so far the instrumentation of the optical engine in the APS LABS by April 2015.