IntroductionAlgorithms of face recognition have long been researched and are maturely developed. However,the issue becomes intractable when these algorithms are implemented on an embedded platformwhich has comparably low computation power.In our project, a face recognition algorithm on a portable device (an embedded platform) isimplemented. A input image will first be captured by the digital camera and is processed by theFace recognizer and the Search engine. Information will be displayed to the user through the Videoeyeglasses interface.

The face recognition algorithm in this project is mainly divided into three parts, Face detection,Face extraction and Face recognition.

The algorithm is implemented on two embedded platforms and is compared with aimplementation in a paper called "Characterization of visual feature recognition“.

Aims and ObjectivesThe project objective is to implement face recognition in an optimum way in terms of run timeonto the embedded system. Various algorithms and methodologies are studied and hardwareresources planning will be done to achieve the goal.This kind of face recognition embedded system can be widely used in our daily life in differentsectors. We hope that human life can be greatly helped with this technology. Some typicalapplications are listed as follows.

Methodology

Methodology (cont’d)Faces are stored in the database for the face-recognition function. At most five faces of differentviewing angles can be stored for each person/record. This leads to the best recognitionperformance if the five faces are captured in the following orientation: -45o, -20o, 0o, +20o, +45o.

Face recognition algorithms flow chartThe Face recognition algorithms are mainly divided into three parts.

User InterfaceFace recognition mode (left): Face recognition function is performedFace record mode (right): New record for a person can be added to the database

ResultsRun time comparison 1:Compare the face recognition algorithm on two difference embedded platforms.

Run time comparison 2:Compare between the embedded configuration in the paper "Characterization of visual featurerecognition“ and the face recognition algorithm in this project on Devkit8000 platform.

Successful rate test:A test on the successful rate is done with 104 test faces, which are different form the 85 facesstored in the database. Threshold is set to identify the unrecorded face. With the threshold is set ataround 2400, a successful rate over 70% can be achieved.

ConclusionThe face recognition algorithms are successfully implemented on the embedded platform. Inaddition, a face recognition software application with a friendly user interface is designed and thenew display interface---the video eyeglasses is used to display. This provides user with a totallynew experience.

To summarize, four major goals of the project have been achieved. They are•An effective face detection and recognition algorithm that can be run on embedded platform withacceptable run time•A face recognition software application with a friendly user interface•An embedded system running the face recognition software application•The video eyeglasses display interface

Instructions

Captured face preview

Sample figure

Camera preview

Information bar

Saved face

Database

Correlation percentage

Instruction

Camera preview

Mode selection

Inputimage/video

Face detection

Face extraction

Face recognition

Identification data

A digital camera is used to capture images, new detectedfaces can be recorded. Information like name,organization, character and hobby with respect to eachindividual can be input and stored in the device. Personalinformation/remarks will be prompted out whenever thesystem recognizes any recorded faces under the display.

Original Image

Hue Transform

Decision Maker

Initial Face Region Detection

Decision MakerMasking

Extract Corresponding region from the original image

Extraction stop

Eigenface DecompositionEigenvectors

Euclidian Distance MeasurementDatabase

Decision Maker

Recorded Face Unrecorded Face

Face Non-Face

Successful

Rate (%)

Threshold

Operations YC2440 DevKit8000

Database setup (10 images stored in the database) 10.7388s 0.476s

Face detection(per frame) 9.853s 0.505s

Face recognition(per frame) 0.267s 0.011s

Embedded platform

Embedded Platform Embedded Platform Improvement Remarks

Operations

Embedded configuration in

"Characterization of visual

feature recognition"

Devkit8000

Flesh tone + Viola detection Flesh tone

2.16s 0.505s 76.6%

Face recognition( per frame) 0.992s 0.487s 50.9% Include database setup time

Face recognition( per frame) NA 0.011s NA Exclude database setup time

Face detection(per frame)

Sector Applications

Business Meeting, Gathering

Education Teaching assistant

Psychology Eye disorder helper

-45o -20o 0o +20o +45o

Sample Input:

Database: