ImageRec Ptype I

This project aims to investigate the efficiency and usefulness of affordable image recognition systems, which could be used in homes or small offices, as an effective security measure. In the present global outbreak of terrorism, such a system could prove to be invaluable to society. This project’s research was done on a Compaq laptop with 256-Mb ram and processor clock speed of 500Mhz. A standard USB PC camera was used for image acquisition, with subsequent image processing being done using algorithms running on a Matlab platform. The preliminary results have shown close to 90% accuracy in face recognition. This suggests that such a system is a viable option for the security conscious public. Furthermore, it could be used for entry detection and digital time stamping, providing a very versatile program for future development. Introduction The September 11 terrorist attacks on the World Trade Center towers 2 years ago have radically changed the way we see security. More recently, there have been the Bali bomb blasts and Jakarta hotel bombings. These terror threats were brought even closer with attempts to sabotage Singapore’s key installations and public infrastructure. As such, the idea of homeland security has been raised to a higher level. Additional security measures for these key installations were clearly needed. An option was to develop a robust image recognition system for rapid deployment. This would be able to minimize the possibility of future terrorist attacks. Thus, we were inspired to work on a viable solution. The idea of identifying humans through facial features has been around for a very long time. However, present systems are expensive and are not cost-efficient. Therefore, it is a challenge to develop a cheap yet effective recognition system. This project aims to fulfil these objectives through the use of widely available software shared freely in the Internet as well as commercial off-the-shelf hardware. However, these software require customization and fine-tuning before it can reach a high level of effectiveness. Method Firstly, we had to select the most suitable algorithm to implement. This was difficult due to the high numbers of algorithms freely available on the Internet. The method used was to compare the recognition rates of the various algorithms. Fairness was achieved by using a common image database as well as similar environmental conditions, such as lighting effects. A few of the well-known methods for face recognition include the Eigenfaces method [1], Hidden Markov Face Model method [2], Principal Component Analysis (PCA) as well as Neural Networking [3]. Much analytical skill was involved in selecting the suitable algorithm. Based on the outcome of the tests, we proceeded using the Eigenfaces method and PCA. 1 SRP student, Hwa Chong Junior College 2 DSTA Engineer, HQ Maintenance and Engineering Support The Eigenfaces Method This method involves utilizing the grouping of facial structures at certain locations in the image space. The premise is that faces have structures, which typically fall into a lower dimension space and lie in a small separate region from other points of interest in the image, thus the shorter vectors would describe face localization. Figure 1 illustrates this graphically. To efficiently describe this cluster of images, we employ PCA. Figure 1: The image-space and face-space coordinate system Principal Component Analysis (PCA) PCA is a standard decorrelation technique and after its application, one derives an orthogonal projection basis, which directly leads to dimensionality reduction, and possibly to feature selection. Experiment After our preliminary study on the various algorithms, we move on to further experiment with the selected method. This experiment can be divided into 3 phases. Phase 1: Acquiring images for the database All images captured were taken with a fluorescent light directly overhead as the only light source. Images used were all frontal and had no masking. The database used in this experiment had 50 classes and 5 images per class. Subject is looking straight into the camera Subject is looking 25 degrees to the right Subject is looking 25 degrees to the left Subject is looking 35 degrees upwards Subject is looking 35 degrees downwards Phase 2: Acquiring image for recognition Images captured for analysis and recognition was taken with a single light source (fluorescent light) located overhead. Images used were frontal and had no masking, with the subject looking straight-ahead, directly into the camera.