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NS0-502 - NetApp Certified Implementation Engineer-SAN and Virtualization - BrainDump Information

Vendor Name : Network-Appliance
Exam Code : NS0-502
Exam Name : NetApp Certified Implementation Engineer-SAN and Virtualization
Questions and Answers : 106 Q & A
Updated On : Click to Check Update
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Network-Appliance Implementation outline

A 0.seventy nine-mm2 29-mW actual-Time Face Detection IP Core | NS0-502 test Questions and PDF Download

Yuichi Hori, Yuya Hanai, Tadahiro KurodaKeio university, Yokohama, Japan


A 0.79-mm2 29-mW precise-time face detection IP core is fabricated in a 0.13-mm CMOS expertise and its efficiency was evaluated. It includes seventy five-kgate common sense, fifty eight-kbit SRAM, and an ARM AMBA bus interface. comprehensive optimization in each algorithm and hardware design improves performance and reduces area and vigour dissipation. Two sorts of templates with facial elements are proposed to obtain excessive pace and yet accurate face detection. a gradual State Genetic Algorithm is employed for high-speed hardware implementation of template matching. To in the reduction of enviornment and energy dissipation, body memory is optimized at minimal and the detection engine is shared for 2 sorts of template matching. The IP core can observe eight faces per body at 30fps. Face detection accuracy is 92%.


Ubiquitous computing society is coming a little way down the street. In such society, tiny computer systems might be dispensed all over the place to establish verbal exchange between them and supply us a simple technique to entry to the network. probably the most computers will play a key function in interface between human and computer systems. As one in all such interfaces, face detection method will shift imaginative and prescient methods like dispensed vision network, digital nonetheless digicam, digital film camera, surveillance digicam and robot eye and so on. to company-new interface for Human laptop interplay.

To be disbursed all over, face detection approach should be not simplest speedy and accurate however also small and low-energy. Small and low-vigour face detection method is proper for transportable buyer functions akin to automatic center of attention, computerized publicity and computerized zoom to faces for digital nonetheless cameras and camcorders, or human detection and head counting for protection and survey functions. in particular, when you consider that our research is targetting to be applied to the digital digital camera of buyer appliance market, the application is aimed to obtain greater than ninety % of accuracy for daily pictures fascinated by moveable digital cameras. additionally, for the reason that the can charge-performance ratio requirements are a lot better, a different purpose is to obtain the in your price range by means of reducing the energy consumption to forty mW which is below 10 % of the total power consumption of an image processing LSI on which our face detection approach is going to be hooked up, and imposing the method within a neighborhood of 1 mm2.

This paper gifts a 0.seventy nine-mm2 29-mW true-time face detection core. desk I suggests the efficiency comparison between our work and previously presented graphic awareness suggestions [1-4]. This desk tells that the method with high efficiency can not be of small area and the method with smaller area can not obtain excessive efficiency. The proposed core can be the only approach for distributed vision gadget in ubiquitous computing society.

table I. efficiency assessment with connected works on picture consciousness.

Fig. 1. Algorithm stream.

Fig. 2. image preprocessing circulation.

Fig. three. dermis-tone in YCbCr color house.

Fig. four. effects of photograph preprocessing.

Face Detection Algorithm

A.   Algorithm Overview

The proposed algorithm consists of three steps (Fig. 1); the first step is for epidermis-tone part extraction, the 2nd step is for coarse face detection and the 3rd step is for specific face detection. in the 2nd and the third steps, template matching is finished by using templates which are product of facial points. for prime pace and accurate face detection, 2 sorts of templates are employed. The 2nd step is “coarse detection”. A lines-of-face template [5] is employed to discover a facial facet within the epidermis-tone edges. the quest enviornment can be unexpectedly narrowed all the way down to restricted areas the place faces can be found. This detection, besides the fact that children, may additionally consist of non-face objects with skin-tone and facial form similar to palms, palms and bushes in backgrounds. The third step is “exact detection”. The restrained areas are explored intensively with a 2d facial template to eliminate the false detection.

B.     photo Preprocessing

Fig. 2 indicates the circulation of photograph preprocessing. because the 1st step of photo preprocessing, the color tips of enter photograph is compressed from 16bits to 3bits per pixel for the aim of inside reminiscence discount of hardware. To in the reduction of the memory greater, 128x96-pixels area is cropped from the middle of the enter image which has 160x120-pixels decision. accordingly, the dimension of frame reminiscence is reduced by way of around 1/10 (307 kbit to 37 kbit). This contributes greatly to reduction in chip area and energy dissipation, because the frame reminiscence often occupies high share in chip area.

After cropping the input image, skin-tone color is extracted from the enter photo as the most essential information of human face. The skin-tone pixels which are extracted manually from daily photographs are plotted like Fig. 3(a). it is favourite that the distribution is nonlinearly dependent on luminance [6]. To extract such nonlinear skin-tone distribution effectively, a three-D colour model which is defined in YCbCr colour space is utilized. The 3-D colour mannequin contains eight rectangular parallelepipeds which are described in every Y latitude to fit to the dermis-tone distribution in YCbCr colour house as shown in Fig. three(b). This filter is essential ample to be carried out in hardware with low vigour and small area compared with implementing the equation presented in [6]. To eliminate tiny noises, Median filter and Opening/Closing filter are adopted. We name the outcome “dermis-tone flag map” which is shown in Fig. four(b).For the graphic preprocessing for coarse detection, aspect extraction and blurring are executed for dermis-tone flag map. This emphasizes the perimeters of epidermis-tone for face candidate detection. Laplacian filter with 5x5 window is adopted to extract edges of dermis tone. Then, entire image is scanned with 5x5 window for blurring. If there is an edge pixel on the center of the window, cost of the center pixel is set to L = three. Surrounding pixels are set to L – i (i = 1, 2, … , L) reckoning on the gap from the center pixel. because of this, 2-bit blurred picture is obtained like Fig. four(c). This blurring can give a margin to lines-of-face detection described below. higher 1 bit is the skin-tone flag map.

For the photograph preprocessing of precise detection, 8 bit to 2 bit luminance conversion is carried out. all through the procedure, luminance normalization is achieved the use of the maximum and minimum luminance value of old frame. higher 1 bit is the skin-tone flag map in the same manner because the photo preprocessing for coarse detection.

C.     Coarse Detection : lines-of-face Detection

This part describes about a good-poor lines-of-face template to observe the fringe of faces from the preprocessed picture effectively. In Fig. 5(a), proposed high-quality-poor traces-of-face template is proven. White elements stand for the effective template to consider existence of the traces-of-face. Black elements stand for the terrible template to evaluate nonexistence of the lines-of-face.

We make use of a semi-ellipse composed of the wonderful templates to notice the edge of faces. moreover, bad templates are laid outdoor the fantastic template. There aren't any bad templates at the bottom of the template as a result of here's neck enviornment. sometime chin area doesn't seem since the color of chin and neck is equal. So we put the high-quality templates carefully there.

Fig. 5. Templates.

D.     exact Detection : Face Judgment

After strains-of-face detection, there could be some final noises because the traces-of-face template can simplest become aware of dermis-tone contour. To eradicate these noises, 2-D facial template is added (Fig. 5(b)). 2-D facial template has a great deal assistance (16x11 pixels) than the traces-of-face template (16 pixels) to know more precise detection. through the use of this template for template matching, the detected enviornment in lines-of-face detection is judged as face or non-face. 2-D facial template is generated via averaging photographs of frontal face for generalization and is blurred to eradicate particular person change.

E.    Genetic Algorithm for Template Matching

A genetic algorithm (GA) [7] is employed for top velocity template matching. fuel use thoughts inspired by evolutionary biology corresponding to inheritance, mutation, natural alternative and crossover. a solution is represented as a gene. The optimization is performed by way of making a pool of genes. In each era, genetic operations such as crossover, mutation, fitness calculation, model and survival are achieved for all genes. Genes are regularly more suitable generation by using technology and finally led to the highest quality solution.

To adopt gas for template matching, suggestions of transformed template is integrated in a 25-bit gene code as proven in Fig. 6. health of gene is evaluated with the aid of calculating correlation between the changed template and the target image. The gene with the highest fitness right through the evolution provides the enviornment within the goal photograph where the photo is comparable to the form of the template.

Fig. 6. Gene code for template matching.

Hardware Implementation

A.     Overview of precise-Time Face Detection Core

A block diagram of proposed true-time face detection core is shown in Fig. 7. The core incorporates 5 accessories; the frame memory, the face detection engine, the graphic preprocessor, the most suitable-fitness monitor and the AHB I/F.

As described in area I, the dimension of the frame buffer is designed with 128x96-pixels decision as an alternative of 320x240-pixels decision which is frequently used via face detection algorithms [8, 9]. here's because experimental consequences exhibit that simplest about 3% lower of the detection price is seen in the case of 128x96-pixels decision in comparison to the case of 320x240-pixels resolution. youngsters, it's greater important to reduce the dimension of frame memory to 1/four. The smaller frame memory contributes to cutting back the core enviornment.

The face detection engine is the main a part of the proposed core and carries 22-kgates good judgment and 12-kbit SRAM. This essential implementation also contributes to reducing the core enviornment and vigor dissipation.

ARM AHB interface module is also set up to recognise normal interface to ARM processor. The input picture is sent from ARM by way of this module. Then the photo is processed with the aid of photo preprocessing module. After that, the face detection engine starts to go looking faces gaining access to to the frame buffer. most effective-fitness monitor assessments the detection effects and ship them to ARM processor.

B.     four-stage Pipelined architecture of Genetic Algorithm

it is proven how template matching is realized the usage of GA in area II, nevertheless there's an issue for top pace operation. in the accepted circulation of common GA (sGA), form procedure is carried out after fitness calculation. The type technique usually requires a huge variety of cycles like order of N to the 2nd energy besides the fact that children other strategies require simplest a few cycles. This factors pipeline stall.

To get to the bottom of this issue, a steady State Genetic Algorithm (SSGA) [10] is employed. A block diagram of the face detection engine is shown in Fig. 8. In SSGA, sort process is omitted and survival system is accomplished in its place of it. In survival technique, health of folks and baby are compared with every other. If a mum or dad is weaker than the child, the father or mother is changed by means of the baby and the infant survives to the subsequent technology. considering the survival technique requires handiest just a few cycles, the hardware can be designed in a four-stage pipeline without the stall problem. although, omitting the form procedure may also require greater generations to attain the surest answer, the elementary hardware implementation raises clock frequency and lowers area and vigor dissipation enormously [11]. So, it will also be talked about that SSGA is rather relevant for hardware implementation in comparison with sGA.

C.     replica Module of consistent State Genetic Algorithm

a reproduction module which is the core part of SSGA hardware carries rather primary circuits as shown in Fig.9 (The grey part of Fig.8). at first, random numbers for guardian selection, crossover mask and mutation mask are generated via Linear feedback Shift Registers (LFSR) which is generally used as a pseudo random quantity generator. because the variety of inhabitants described within the core is 256, 8-bit LFSR is adopted for the mother or father particular person selection. The crossover masks and the mutation mask are 25-bit shift registers. The assessment influence between configurable thresholds (crossover rate and mutation rate) and the outcomes of sixteen-bit LFSR is substituted because the first little bit of each and every masks. 16-bit LFSR that could generate greater specific random numbers than eight-bit LFSR is used to manage the incidence of the crossover and the mutation more strictly.

The gene of “guardian 1” is made by means of loading parents' information from pool of genes by using a random quantity got via 8-bit LFSR. the use of gene of “mother or father 1” and gene of “parent 2” which is received with the aid of a random quantity generated on the next cycle, new gene is generated. If the crossover mask is one, gene bit of “guardian 1” is loaded to new gene bit. If the crossover mask is zero, gene little bit of “guardian 2” is loaded to new gene bit. This system is known as as crossover. additionally, if the mutation mask is one, the corresponding gene is reversed to the gene received by crossover. This process is the mutation. a new gene which is generated through crossover and mutation using gene of folks is the baby gene to continue to exist within the subsequent generation. by using continuing this cycle, the pool of genes is regularly advanced.

Fig. 7. Block diagram.

Fig. eight. Face detection engine.

Fig. 9. Circuit schematic of reproduction module

Experimental outcomes

The proposed core with the ARM9 processor is fabricated in a 0.13-mm CMOS expertise as an inside block of a conventional goal photo processing LSI. A chip photomicrograph is proven in Fig. 10 and measured performance is summarized in desk II. as the result of processing time measurement through running proposed algorithm on a pc with Pentium4 3.eight GHz and a pair of-GByte leading memory, it took about four hundred msec to conclude processing each image. considering the core can complete its processing in 33 msec, it may also be said that the core is in a position to processing 10 times faster than the Pentium four, despite the fact that the area of the core is barely 0.79 mm2. in keeping with the outcomes of statistical scan with the aid of simulation, the method time is 1.5 msec for the coarse detection and a pair of.5 msec for the genuine detection on average. This consequences exhibit that the core can become aware of 8 faces within a frame.

desk II. efficiency summary.

Fig. 10. Chip photomicrograph.

To ascertain actual-time operation of the core, an comparison board is developed as proven in Fig. 11. The chip is installed on the leading board and the front-conclusion board with charge Coupled equipment (CCD) sensor and a fixed focal size lens is related to it. using this board, it is demonstrated that 8 faces may also be detected in each body of moving photos at 30 frames/2nd. furthermore, bendy software may also be developed by using connecting ARM debugger and loading a code to the chip.

Fig. 12 summarizes experimental outcomes on face detection accuracy. As a verify photo set, 200 photographs together with 339 faces from every day scenes are chosen at random. This test picture set includes various lights environments, which includes day and night; indoor and out of doors (solar light, incandescent lamp, and fluorescent lamp, and so on.). The face detection price is 92 % on commonplace. This detection price is achieved in the latitude that covers 90 % of the variety of faces in the verify image set, each for dimension of face, for perspective of rotation and for angle of path. Faces that are small and searching up/down-ward or sideways in day by day pictures can even be detected. The variety of detected non-face object per photograph is less than 0.5 even with photographs of out of focus, below/over publicity and lot of obstructions. one of the vital detection effects are shown in Fig. 15.for instance of utility for proposed face detection core, head counting is proven in Fig. 13. Two experiments are conducted; (a) counting variety of people walking in a station with the aid of a gradual camcorder, and (b) counting variety of americans sitting in a stadium by using panning a camcorder. within the test (a) where around one hundred individuals stroll throughout in entrance of the digital camera per minute, counting accuracy is 87 %. within the test (b) the place around 1,500 americans are shown up on the pictures per minute, counting accuracy is 86 %.

Fig. eleven. assessment board.

Fig. 12. Experimental results on face detection accuracy.

Fig. 13. Head counting.

As another application, object detection is proven in Fig. 14. The proposed core is designed flexibly so that the template, skin-tone filter, GA parameters and so on may also be configured with no trouble via ARM9. for instance, colour ball detection is shown here. by using environment skin-tone filter for the color of the target ball and lines-of-face template to a round kind, the ball can simply be detected. If goal form is primary satisfactory, e.g. a circle, a rectangle, a eclipse and so on, object detection can be realized at frame cost of 60 frames/2d as a result of there’s no necessity to run judgment method.

Fig. 14. Object detection.


A 0.seventy nine-mm2 29-mW actual-time face detection core became fabricated in a 0.13-mm CMOS know-how and its efficiency turned into evaluated. It includes 75-kgate logic, fifty eight-kbit SRAM, and an ARM AMBA bus interface. comprehensive optimization in each algorithm and hardware design improves efficiency and reduces area and energy dissipation. Two forms of templates with facial points are proposed to obtain excessive speed and yet accurate face detection. a steady State Genetic Algorithm is employed for high velocity hardware implementation of template matching. To in the reduction of enviornment and vigor dissipation, body reminiscence is optimized at minimal and the detection engine is shared for 2 forms of template matching. The core can notice eight faces in each body of moving photographs at 30 frames/2d. Face detection accuracy is ninety two %. a part of the longer term work is to replace the current core through multi-core so that the accuracy and the robustness for size of face, path of face and rotation of face may also be additional stronger with amount of boost in chip area.


This work is supported by means of a supply from NuCORE know-how Inc.. The authors are very grateful to the enterprise for advantageous comments and positive discussions.


[1] k. Korekado, T. Morie, O. Nomura, T. Nakano, M. Matsugu and A. Iwata, “an image Filtering Processor for Face/Object attention the use of Merged/combined Analog Digital architecture,” in IEEE Symp. VLSI Circuits Dig. Tech. Papers, pp. 220-223, June 2005.

[2] T. Theocharides, G. hyperlink, N. Vijaykrishnan, M. J. Irwin, W. Wolf, “Embedded Hardware Face Detection,” in Proc. IEEE Int. Conf. VLSI Design, pp. 133-138, Jan. 2004.

[3] T. Kozakaya and H. Nakaia, “development of a Face attention system on an image Processing LSI Chip,” in Proc. IEEE Conf. computing device imaginative and prescient and demo attention Workshop, vol. 5, pp. 86, June 2004.

[4] k. Imagawa, okay. Iwasa, T. Kataoka, T. Nishi and H.Matsuo, “real-Time Face Detection with MPEG4 Codec LSI for a cellular Multimedia Terminal,” in Proc. IEEE Int. Conf. buyer Electronics, pp. sixteen-17, June 2003.

[5] Y. Hori, k. Shimizu, Y. Nakamura and T. Kuroda, “a true-Time Multi Face Detection technique the usage of fantastic-poor lines-of-Face Template,” in Proc. IEEE Int. Conf. pattern cognizance, vol. 1, pp. 765-768, Aug. 2004.

[6] R.-L. Hsu, M. Abdel-Mottaleb and A. ok. Jain, “Face Detection in color images,” IEEE Trans. pattern analysis and machine Intelligence, vol. 24, no. 5, pp. 696-706, may also 2002.

[7] J. H. Holland, “Adaptation in natural and artificial device,” college of Michigan Press, 1975.

[8] P. Viola and M. Jones, "amazing precise-time face detection," Int. J. computing device vision, Vol. fifty seven, challenge 2, 137-154, may additionally 2004.

[9] C. Huang, H. Ai, Y. Li and S. Lao, "Vector Boosting for Rotation Invariant Multi-View Face Detection," in Proc. IEEE Int. Conf. computer vision, pp. 446-453, Oct. 2005.

[10] G. Syswerda, “Uniform Crossover in Genetic Algorithms,” in Proc. Int. Conf. Genetic Algorithms, pp. 2-9, June 1989.

[11] B. Shackleford, G. Snider, R. J. Carter, E. Okushi, M.Yasuda, okay. search engine marketing and H. Yasuura, “A high-performance, Pipelined, FPGA-based Genetic Algorithm computing device,” J. Genetic Programming and Evolvable Machines, vol. 2, no. 1, pp. 33-60, Mar. 2001

Fig. 15. Detection effects.

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