Design and Implementation of a Face Recognition System-on-a-Chip for Wear able/Mobile Applications
Design and Implementation of a Face Recognition System-on-a-Chip for Wear able/Mobile Applications
Journal of Korea Multimedia Society. 2015. Feb, 18(2): 244-252
Copyright © 2015, Korea Multimedia Society
  • Received : January 07, 2015
  • Accepted : February 13, 2015
  • Published : February 28, 2015
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About the Authors
Bongkyu Lee
Dept. of computer science and Statistics, Cheju National University

This paper describes the design and implementation of a System-on-a-Chip (SoC) for face recognition to use in wearable/mobile products. The design flow starts from the system specification to implementation process on silicon. The entire process is carried out using a FPGA-based prototyping platform environment for design and verification of the target SoC. To ensure that the implemented face recognition SoC satisfies the required performances metrics, time analysis and recognition tests were performed. The motivation behind the work is a single chip implementation of face recognition system for target applications.
Face Recognition (FR) is an integral part of wearable/mobile systems like memory aids or context awareness systems. Thus, developers will integrate FR system with clothing, accessories and mobile phones. For instance, FR systems embedded into eyeglasses can help us remember the name of the person we are looking at by recognizing their face from a stored database of images and “whispering” the person’s name into our ear [1] .
Although many FR techniques have been proposed thus far [2] , FR system for wearable/mobile applications still remains to be a challenging problem. Wearable/mobile computing devices have constraints in their processing capabilities because of cost and power consumption, while generic FR systems ( Fig. 1 ) require a large amount of resources for sufficient accuracy [2] . Also, since wearable/mobile devices should be as light and as small as possible, an embedded FR system organized on a Printed Circuit Board (PCB) are inappropriate for wearable/mobile applications.
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Configuration of a generic FR system.
A System-on-a-Chip design methodology can be used to integrate entire components of a target system into single chip so that it can be applied to one-chip implementation of FR for wearable/mobile applications with compact size and weight.
In this paper, we propose a new FR SoC architecture for wearable/mobile computing and applications. The implementation steps are as follow. First, we designed and implemented the prototyping emulation platform based on a Field Programmable Gate Array (FPGA). This emulation platform was used for testing and debugging the target SoC architecture in the register transfer level. We performed a comprehensive analysis of the computational characteristics of a FR algorithm on the emulation platform. Then, we selected some software modules that should be implemented into hardware because of their intensive computing requirements. We designed hardware logics for selected software modules and mapped them into the FPGA. Other modules of the FR algorithm were stored into the memory that will be implemented into a final SoC. We evaluated the performances of the implemented FR SoC in the register transfer level using the emulation platform and observed that the implemented FR SoC can be used for various wearable/mobile applications.
Embedded FR systems can be mainly divided into three categories; fully software implementation [3 - 5] , fully hardware implementation [6 , 7] and SoC design method [8 - 10] . A fully software implementation is more flexible, easier in developing and debugging. However, this method usually requires higher hardware capabilities such as a powerful processor and a large amount of memory. Therefore, this method is not suitable for mobile/wearable applications which have only restricted hardware resources.
A fully hardware implementation is characterized by its high speed, concurrency and lower power consumption. In [6] and [7] , the face detection stage and feature extraction stage were implemented using this method. However it is difficult to design and debug compared to other methods. Also, pure hardware implementations of the feature extraction and recognition stages are not effective in terms of cost and implementation complexity compared to other methods [3 , 9 , 10] .
The SoC method is carried out by analyzing the timing of the different portions of the algorithm and implementing the time-extensive parts as hardware. This method gives the capability of integrating different functions into a chip. In [9] , the face detection stage was implemented onto a reconfigurable hardware according to the SoC method. However, the implementation only aimed at face detection so that it cannot be used for FR applications directly. In [8] and [10] , they designed a set of fixed-point custom hardware instructions into a FPGA for time consuming functions, such as square root and arc tangent. Since the custom hardware instruction-based optimization of the FR algorithm reduced the execution time under an embedded environment, these approaches have many advantages in terms of flexibility and efficiency. However the former implementation [8] [10] cannot be directly applied to real FR applications, since their approaches were not considered ‘real-life’ environments [3] .
- 3.1 The prototyping platform and the operating software
To design and verification of SoC, FPGA-based emulation platform has become popular in co-verification and rapid prototyping [11] . Mapping the entire design of the target SoC into an FPGA gives an accurate and fast representation.
For the prototyping platform based on a FPGA, the basic components, - including CPU, several system buses and associated interconnection blocks, - were selected according to the design steps. We used LENO2 (32-bit RISC processor) [12] and its related Floating Point Unit (FPU) for base processors. Register transfer level modules of processors were implemented into the FPGA (XILINX X2CV8000). The operational clock rate of processors was 30MHZ. For acquisition of images, a MICRON MT9V112 image sensor [13] was connected to the emulation platform using an I 2 C bus which was implemented in the FPGA.
The emulation platform has the SDRAM-based memory (128 Mbytes, SAMSUNG 16bits × 2) and the Flash-based storage (8 Mbytes, INTEL STRATA 16bits × 2). SDRAM-based memory unit is used for storing images captured by the image sensor, while Flash-based storage is used for storing codes (program) and data. Fig. 2 shows the implemented emulation platform with compact size of 112 × 129 mm.
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The prototyping emulation platform.
We designed small interactive bootstrap program for operating the emulation platform. Fig. 3 shows the flow of the bootstrap program. First, it initializes the hardware components, such as I/O interfaces, memory, serial port units and timer/interrupt units. After hardware initialization, the program waits for the command produced from external buttons. The emulation platform has 4 button inputs (commands); enrollment, recognition, debugging and reset. The program analyzes received commands, and executes the appropriate routine for that command. By using this program, the overhead of operational software was greatly reduced.
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The monitor program.
- 2.2 The FR algorithm
Fig. 4 shows the FR algorithm that was used for design and implementation of the FR SoC. It was executed and analyzed on the emulation platform. The algorithm consisted of three stages; the face detection stage, the feature extraction stage and the recognition stage. The face detector consisted of two multi-layer perceptrons, the fast search network and the fine search network. In [14] , they showed that neural networks can effectively used to face detection task. The structure of each network was three-layer (input/hidden/output) and fully connected perceptron.
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Block diagram of the FR algorithm.
The fast search network searches face candidates (30 × 30 sized region) in input images, while the fine search network is for searching actual face regions (size of 20 × 20 pixels) among face candidates obtained by the fast search network. Input nodes of the fast search network receive original intensity and histogram equalized intensity of each pixel so that the number of input nodes is 2N, where N is the dimension of input vectors. Input nodes of fine search network use histogram equalized intensity and lightening corrected intensity of each pixel so that the number of input nodes is also 2N. Position relative feature vectors [15] of nose, mouth and lips of each detected face were extracted for recognition. Covariance distances between an input image and enrolled DB entries were measured for recognition. The algorithm was implemented by building a C program. The original algorithm had showed 93.4% of the face detection rate and 89.0% of the recognition rate with FERET [16] face database.
Since the FR algorithm should be executed on the emulation platform, we have employed many engineering solutions into the original FR codes. A floating point computation was converted into an integer computation by normalizing the operands into an integer under certain operator. In some cases, multiplication was converted into addition. Also some intrinsic functions, such as cosine and hyper tangent, were converted into table lookups. After modifications, final C codes were translated into an executable codes for LEON 2 using BCC-bare C cross compiler [11] .
- 2.3 Timing analysis and partitioning
Next step in the design process was to perform timing analysis for the different software functions of the algorithm on the emulation platform. We analyzed the computational requirements of the modified FR codes under a face database which has 200 images of 20 persons. 320 × 240 sized images were captured in the environment of large variances in illumination and poses. Three images of each person were used for enrolling so that the experimental DB contained 60 images. Calculating the elapsed time taken by each function was used to select time-consuming modules of the algorithm. We did this analysis by inserting software timestamps between the functions.
After performing this step, we found out that the face detection phase took 89% (89 seconds) of the entire processing time (100 seconds). The face detection phase consisted of four modules (as shown in Fig. 4 ), image pyramid generation, histogram equalization and lightening correction and two neural-based detectors. Among them, two neural network based face detectors consumed 76% (68 seconds) of the entire processing times for face detection phase. The image pyramid generation routine took 17% (15 seconds) of the entire processing times for face detection phase. Histogram equalization and lightening correction required 7% (6 seconds) of the total elapsed time (100 seconds).
Generally, a neural network requires M × N × K times of ‘Sum Of Products’ (SOP) [15] , where M is the number of output nodes, N is the number of hidden nodes and K is the number of input nodes. This intensive computation requires powerful processors so that it is difficult to satisfy real-time processing requirement with slower embedded processor. Making image pyramid in real time was also difficult with the target embedded processor. Histogram equalization and lighting correction modules did not require as much times as the above two modules, they also required extra times for their own tasks. As a conclusion, these four modules should be implemented as hardware logics in the SoC.
In this section, we have focused on the design and implementation details of two hardware blocks for face detection phase
- 2.1 The pyramid reduction block
Fig. 5 -a shows the architecture of the hardware block for production of a sequence of reduced images of an original image, called image pyramid [14] . A generated image pyramid is used for finding faces that are larger than the size of predefined processing window, which are 30 × 30 (in the case of the fast search) or 20 × 20 (in the case of fine search). Since continuous reductions with respect to the original image can be achieved by smoothing the image with a smoothing kernel and the scale parameter, the block used the scale factor 1.25 and 3-tap Gaussian filter. The block receives a 320x 240 sized image stored in SRAM-based memory and constructs an image pyramid using internal working memory A and B as shown in Fig. 5 -a. The generated image pyramid is stored into the SRAM-based memory for the FR coprocessor block, the face detector. Fig. 5 -b shows the control path of the block.
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The pyramid construction block.
- 2.2 The FR coprocessor block
Fig. 6 -a. shows the block diagram of FR coprocessor block, the face detector. It consists of two hardware logic blocks, the preprocessing block and neural network computation block. The preprocessing hardware performs histogram equalization and lighting correction tasks of input images. It performs both histogram equalization and lightening correction for the fine search phase, while performs only histogram equalization for the fast search phase.
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Face detection coprocessor.
The neural network logic block receives its inputs from preprocessing sub-block and computes the output values of hidden and output nodes. First, all output values of hidden nodes are calculated by SOP between input nodes and input-hidden weights. Then, the output value is calculated by SOP between outputs of hidden nodes and hidden-output weights. Weight values were stored as 12 bits precision in the internal ROM. The activation function (hyper-tangent) for each node was computed by table-lookup method, which the table was stored in the ROM with 8 bits precision. Fig. 6 -b shows the control path of the co-processor for performing the task.
The preprocessing block and the neural network computation block are operated simultaneously using two buffers ‘A’ and ‘B’. The preprocessing hardware reads the image from buffer ‘A’ and starts its tasks. During the processing with an image from the buffer ‘A’, another 30x30 image is stored in the buffer ‘B’. When the preprocessing block completes tasks with an image in buffer ‘A’, it transfers the result image to neural network block and read an image in buffer ‘B’ for the next process. The neural network block starts its task simultaneously when the preprocessing block starts with an image in the buffer ‘B’. Fig. 7 illustrates the pipeline processing between two hardware blocks.
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The pipeline scheme between sub-blocks.
All hardware modules were fully synthesized by VHDL model and mapped into FPGA. Fig. 8 shows the chip level design, which has processors, system buses and two FR related blocks at the register transfer level. It works as the prototyping chip of the target FR SoC.
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The block diagram of architecture level design.
The SoC performs full FR phases itself without additional hardware or software. When enroll or recognition button is pressed, a FR task will be performed as following steps. The camera captures a facial image and transmits it to the SRAM-based memory. Then, face detection phase is performed by two hardware logics, pyramid reduction and face recognition co-processor. Detected face regions are stored into SRAM-based memory for next phases. After the face detection stage, the feature extraction and the recognition phases are performed using software modules stored in the Flash memory. Recognition results are finally displayed on the LED.
For the evaluation of the implemented system, we performed recognition experiments using seven images of each person with our experimental DB (60 images for 20 persons). Recognition rates, face detection rates and required times were compared to the fully software implementation. Table 1 shows detection rates, recognition rates and required tines of two methods.
Evaluation results of two methods
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Evaluation results of two methods
The implemented SoC is lower detection and recognitions rates compared to the fully software implementation method. This is because of precision parameters of the implemented Soc such as integers, floats and others. However, the implemented SoC only required 12 seconds, since the face detection time can be reduced to less than one second by the hardware implementation. Although feature extraction and recognition phases required 7.0 and 4.0 seconds respectively, we verified that reduction of times for feature extraction and recognition can be obtained by higher operational clock speed of the processors. Thus, the implemented FR SoC can be effectively used for various real-time applications.
In this paper, we designed and implemented a new FR SoC architecture for wearable/mobile applications. The FR SoC was tested and verified in the register transfer level architecture using FPGA-based emulation platform. All components of hardware and software were directly integrated into the implemented SoC. This FR SoC can be effectively used to give intelligence to wearable/mobile systems. We are going on the chip fabric process for our FR SoC. Also, we hope to improve our FR algorithm for achieving better recognition rates and speed.
Bongkyu Lee
He received the Ph.D degree from the Department of computer engineering, Seoul National University in 1995. Since 1996, he has been a professor at the Jeju national university. His interesting research fields are in Neural Networks, image processing and Augmented Reality.
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