FaceAccess — A Portable Face Recognition System Using Atmega644
We created a standalone face recognition system for access control. Users enroll in the system with the push of a button and can then log in with a different button. Face recognition uses an eigenface method. Initial testing indicates an 88% successful login rate with no false positives.
There are currently commercially available systems for face recognition, but they are bulky, expensive, and proprietary. Our goal was to create a portable low-cost system. Our design consists of an Atmel ATmega644 8-bit microcontroller, a C3088 camera module with an OmniVision OV6620 CMOS image sensor, Atmel’s AT45DB321D Serial Dataflash, a Varitronix MDLS16264 LCD module for output, a 9-volt battery, and a small wooden structure for chin support.
High Level Design
The training process is the only time we use a computer; once this step is complete, the system is completely standalone. If we were to sell this system as a consumer product, we would ship the system pre-trained. The training process consists of teaching the system to key in on the most important features of a face. To do this, a large number of facial images are taken and sent to Matlab to help the system determine the distinguishing features of a face. We use Matlab to create the eigenfaces, which are the principle components of the training set (See Principal Component Analysis). Because the images were too large to be held on the microcontroller, we captured an image a line at a time, sending each line to flash before the camera sends the next line. We then send the image through the microcontroller to Matlab over the serial port. Once all of the training images are in Matlab the eigenfaces and average face are created (see Background Math for more information). Finally, the eigenfaces and average face are sent to flash memory through the microcontroller. Once the eigenfaces and average face are in flash, the system is completely standalone.
Before a user can login to the system, he first needs to register his face and enroll it into the database. To enroll into the system, the user presses the enroll button, which will capture the image. Before the image is captured, the current number of system users is checked; if the maximum number of users is met the new user cannot be enrolled. We set our maximum to 20 users. If the maximum hasn’t been met, the user’s face image is captured and is once again sent to flash memory 176 bytes at a time (same as in the training process) and is stored there temporarily for calculation. The image and eigenfaces are all pulled back to the microcontroller 528 bytes at a time to calculate the new user’s “template”, which is a short vector describing the user’s correlation with the eigenfaces. (see Background Math for more information). The template is then compared with the previously stored templates; if the new template is too close to a previous template, the user cannot be enrolled. We defined the “closeness” between two templates as the cosine of the angle between them. If there are no matches, the new template is added to the database in flash memory to save it in case of a system reset.
The Logging In process is initially very similar to enrolling. The user presses the login button to take his picture, store it in flash memory and begin the logging in process. Again the newly captured image and eigenfaces are pulled from flash back to the micrcontroller 528 bytes at a time to calculate the user’s template. This template is compared with all of the previous templates. For the user to be logged in, their template needs to “match” (be close enough to) only one saved template; otherwise they will be “denied access”. Again, the cosine of the angle between the two templates is used to determine template match. Whether or not the user was logged in, the top three matches are displayed on the LCD.
We currently only have the system setup to have up to 20 enrolled users. However, for demo purposes we wanted the ability to enroll new users and erase the old ones. To do this we added a final button on the back of our protoboard for this purpose. The button needs to be held down for an entire second before the function to erase templates is called. A message is displayed to the LCD to let the user know that the enrollments have been erased.
For communicating with flash memory we used the Serial Peripheral Interface (SPI). For programming the camera we used the Inter-Integrated Circuit (I2C) interface. The video signal was digital, so it was not NTSC, but understanding the NTSC standard helped us understand the camera output.
The eigenface method is in the literature, so we have no intellectual property concerns. We plan to try to publish our results, so we are fully disclosing our design.
When one thinks of face recognition, one immediately thinks of finding features of a face: eyes, nose, ears, cheek bones. But who’s to say that these are the most distinguishing characteristics of a face, and that they are the best features by which a face should be described? And what if these features are correlated? Instead of hard-coding features for detection, we decided to find the orthogonal features that most optimally describe our large training set by using Principal Component Analysis. This creates an orthogonal basis of principal components for our training set. The basis vectors are known as eigenfaces, and can be thought of as characteristic features of a face. All new faces will be described as a linear combination of these eigenfaces. This is equivalent to projecting the new face onto the subspace spanned by our eigenfaces. By using only the eigenfaces with the highest eigenvalues, we ensure that our projection maintains the most face-like energy from the image. This method was proposed by M. Turk and A. Pentland in 1991.
The system is divided into three different processes: Training, Enrolling, Recognizing.
The training portion of the system consists of creating the eigenfaces based on a set of training faces. The goal was to create as many eigenfaces as possible and keep the M eigenfaces that had the highest eigenvalues.
Given N training images, create a matrix where each column is a face vector of length 176 * 143 = 25168.
We used a total of N=40 training faces to create the eigenfaces. Below are the raw images captured (before any processing) of some of our volunteers; not all of these faces were used to create the eigenfaces.
|Part||Source||Unit Price||Quantity||Total Price|
|ATmega644 (8-bit MCU)||ECE 4760 Lab||$8.00||1||$8.00|
|C3088 with OV 6620 (Image Sensor Module)||ECE 4760 Lab||$0.00||1||$0.00|
|AT45DB321D (Serial Flash)||DigiKey||$3.87||1||$3.87|
|Varitronix MDLS16264(LCD Module)||ECE 4760 Lab||$8.00||1||$8.00|
|PC Board + Power Regulator||ECE 4760 Lab||$0.00||1||$0.00|
|Wood & Screws||Lowes||$5.00||1||$5.00|
For more detail: FaceAccess — A Portable Face Recognition System Using Atmega644
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