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Journal of Science* Technology 101 (2014) 159-163 Access Control Using Face Recognition Pham Thi Thanh Thuy\ Le Thi Lan, Dao Trung Kien, Pham Ngoc Yen Hanoi University ofScience and Technology No Dai Co Viet Str, Ha Noi Viet Nam Received' December 10, 2013; accepted: April 22, 2014 Abstract In this paper, we propose an access control system using human face recognition Basically, the system composes of two mam parts- face recognition and door control The contnbution of our paper is the automatic access control system which uses face recognition method based on LBPH (Local Binary Pattern Histogram) feature matching and classification of minimum distance decision mle The advantages of LBPH features Is that they have high speed of matching and suitable for real-time face recognition applications We have compared the performance of the face recognition method using LBPH with two state of the art methods based on LDA (Linear Discriminant Analysis) and PCA (Principal Component Analysis) The expenmental results show that the proposed method outperforms the state of the art methods Based on this result, m order to open door automatically, we design a hardware circuit which connects a computer with an electncal door This circuit has the function of a nornial lock It controls the door to be opened or still closed based on the face recognition result Keywords Face recognition, LBPH features, access control Introduction Biometric identification is applied m man\ securit\ surveillance and access control systems In companson with other biometnc features such as ms or fingerprint human face recognition has richer data sources and requires less controlled mteraution Human face recognition has attracted many researchers over decades howe\er this is still -i problem ot challenges One of them is human face feature extraction Optimal selected and high discnrm native features result to good object recognition with low computational cost and beneficial to the real hme processing svstem Two approaches are proposed for finding optimal face features, those are local and global teature extraction The last one, such as PCA or LDA, focus on the whole face image, otherwise the first one, for example LBP or Gabor features [1,2], uses some unique identifying features on human face As a result, human face recognition results using global features are normally not as good as local features [3] speed of matching and suitable for real-time face recognition applications Moreover, they are less sensitive to illumination variaUons [4], LBPH features are extracted in the detected face patches that are processed in this paper help build high distinguishable feature vectors and give better result for later classification We also design a hardware circuit which connects the computer with an electrical door This circuit has the funchon of a normal lock When a "legal key" of the owner's face IS available, it will unlock door automatically Automatic access control using human face recognition The system includes an IP camera which captures video streams, a hardware circuit which is connected to a computer through COM port, a computer which acquires and processes video streams, recognizes human faces and controls hardware circuitto unlock door (Fig.l) The contnbution of our paper is the automatic access control system which uses face recognition method based on LBPH feature matching and classification of minimum distance decision rule The advantages of LBPH features is that they have high Fig Access control system using face recognition ' Conespongding Author: Tel, (+84) 915 651.748 E-mail: thanh-thuy pham@mica edu.vn Face recognition and door opening control modules will be explained in detail in the following sections [^^M Journal ofScience & Technology 101 (2014) 159-163 2.1 Face recognition module Face recognition module basically includes five steps of processing' input image acquisition, face detection, face image preprocessing, face feature extraction and recognition (Fig, 2) The last one covers two phases: training (learning) and evaluating (testing) In the training phase, the system will be learned from face image database, then individual facial model will be established In the evaluating phase, the system will give the result of identification/verification for the testing facial image based on the trained models Input image acquisition: Video streams are captured from IP camera and each frame will be processed for face detection Face detection: We use Haar like features and Adaboost cascade classifier algonthm for face detection [5] The results of face detection are face patches (region bounded by red rectangle in (Fig 3), In order to remove redundant information which can affect the later face recognition process, a face patch only includes the region of eyebrows, eyes, nose and mouth Face Image Preprocessing: The preprocessing techniques are applied for fece patches so some major challenges for face recognition can be solved First, the original face images have to be converted to the grayscale form Then, histogram equalization techmque is applied to tackle illumination vanations [6], Face patches are also aligned to adjust orientation and resized to 100x100 pixels [ •rizrH RecogniliDD 1 Evaluating In this paper LBPH features are used for face description Instead of using entire face patch, we extract LBP local features from this The basic idea of LBP method is to summanze the local structure in a face patch by comparing each pixel with its neighbors Taking a pixel as center, if the intensity of center pixel is greater-equal its neighbor, then denote it with and if not (1) The result is a binary sequence Using surrounding pixels we will end up with 28 possible combinations, called Local Bmary Pattems (Fig 4) ifi^P,.-I^(g.-gc)2' \, if T>0; 0, otherwis (1) Where LBPRR is LBP code with P sampling points on a circle of radius of R, gc is the grey value of the center pixel and gp is the grey value of P sunoundmg pixels LBP local feamre s are implicit lowdimensional and simple in computation Thus, it is possible to analyze face image in challenging realtime settings Besides, they are more robust against variations in pose or illumination (Fig.5) than holistic methods such as PCA, LDA [8] Image Preprocessing r-j \ these face patches are taken from different camera alignment, or clustered background Face feature extraction provides dimension reduction, salient feature extraction and noise removal After this process, each face patch is converted to a vector with fixed dimension Feature Tnucmg / Fig An example of LBP computation [7] Fig Human face recognition system Fig Face defection result (region bounded by red rectangle) Feature Extraction: It is not effective if we use face patches directly for face recognition The computational cost for this is too high, moreover Fig LBP images with different transformations gray-scale Journal ol Science & Technology 101 (2014) 159-163 prepared traming data set and the results for face classification are given using mmimum distance decision rule 2.2 Door access control module oCfKcimagt The result from face recognition is passed to door access connol module This module lake two tasks- input validatins and door open controlling (Fig.7) ffomeachblock Fig Face description with LBPH Fig Access control After calculation of LBP face patch, we divide il to 64 sub regions (8x8) and extract histogram from each (Fig 6) A feature vector is then obtained by concatenating the local histograms These histograms are called Local Binary Pattems Histograms (2) ^, = E ^ f.i^>y) = = 0,, (2) Where f(x,y) is LBP of face patch or LBP of labeled image; n is the number of different labels produced by the LBP operator; l[A] is I if A is true and if A is false This histogram will be normalized to get a coherent description for face classification (3) ' E:X Recognition: Training data of different preprocessed face patches is created and stored in a folder It has a single XML file that contains tags for the name of person and a file name for the traming image Each image is saved using a random number so that unique file identifiers can be generated This prevents images being overwritten and easily allows several images for one individual to be acquired and stored with no problems In evaluating process, testing images (face patches that are taken from video frames) firstly preprocessed, then their LBPH features are extracted and feature vectors are established These vectors will be compared with feature vectors in previously The biometric face recognition includes two parts: identification (recognition) and verification (authentication) Given the testing face image, the first one points out the answer for the question "Who is he''" That means the system will look in the traming database for nominated face which is the most similar with the probe face, for example he is subject A The last one venfies the submitted face is true or not (Is it subject A'') This is done by threshold validation, A threshold is established by experimental data If the distance of testing feature vector is higher than threshold, the verification result is unknown person, otherwise it is an authenticated one or a person who is contained m trainmg database If the ID of the authenticated person is identical to the room's owner ID (validating process), this module will activate the hardware circuit to open electrical door automatically (door open controlling), otherwise It is still closed In order to open door automatically using the room owner's face, we set a Mmer of seconds This parameter can be sel based on the application requirement During this time, if the face recogmhon accuracy is above 90%, the door access control module will active the hardware circuit lo unlock the door, Experimental results 3.1 Experimental Environment We evaluate the sysiem in our showroom (Fig 8-a) In this room, we set a smart room with an electrical door and a camera IP (Axis Ml054) which is attached on one side of the door (Fig 8-b) Fig (a) Layout of showroom with a smart r inside: (b) an IP camera and electrical door Journal of Science & Technology 101 (2014) 159-163 For the face recognition, we take two phases: training and testing The n-aining face database is acquired by the above mentioned camera All subjects were requested to stand at a predefined distance from the camera system (aboul lm) In the testing phase, we test the sysiem with two types of dataset- probe set and gallery set The gallery set contains images of subjects participated to training images acquisition process while probe set contains images of not only subjects participated to training images acquisition process but also new subjects The testing subjects are required lo stand opposite to the camera aboul one meter and look straight into the camera Fig 10 Ten images of one subject in traming Table I The result comparison of three different methods (a) face recognition for gallery set; (b)face recognition for probe set; (c) computational lime for face recognition phase LBPH 3.2 Measurements In order lo evaluate the performance of the system, we use two cnteria as follows FAR Sensibility 83 91.06 IDA 11.49 89 67 11! 52"*" 1 86.6'! (b) Procassing T i m e TP + FP TP TP + FN Where TP (True Positive) is the number of conectly recognized faces; FP (False Positive) is the number of wrongly recognized faces and FN (False Negative) is the number of lost recognized faces, IM1 iiteconds/Framal LBPH 12.5 LDA 15.7 PCA 18.7 b Access control results Companng with two other state of the art methods PCA and LDA, the LBPH has the highest precision and lowest time consuming for face recognition processing (using the compuler of Intel® core(TM) duo, CPU T5870(g2.00GHz, RAM 2GB) The performance of the door access control is evaluated in the time interval of Al=2s, Dunng this time, about 10 video frames are processed for face recognition In each period of two seconds, if the owner of the room is authenticated with the accuracy of above 90% (equivalent to more than video frames with human faces recognized correctly), the door will be opened automatically, otherwise it remains closed Because during input image acquisition, there are some first frames have bail recognition results, caused by the fact that identified objects have unready postures when they come to the camera, so we inihahze a count variable In the interval of At, with each time of correct recognition for the room o-wner, this variable will be increased by one, otherwise it will be reduced by one The door will be unlocked if the count variable is accumulated to in this interval or it is still closed As a result, for 25 testing subjects, the door is opened for 20 subjects in gallery sel and closed for unknown subjects Fig Face unages of 20 subjects mframingdatabase The result of experimentation shows promising results obtained fi'om our sysiem, However, these are only evaluated in confroiled environment In the fiiture work, we would like to develop an effective human identification model in more complex conditions This could be done by 3.3 Results a Face recognition results For face recognition evaluation, we prepare a training data set of 200 face images of 20 subjects, 10 images for each subject with different head poses and face expressions (Fig, 9, Fig 10) 20 subjects for gallery set and 25 (including strange subjects) for probe set are used in the testing The face recognition results are showTi in Table for gallery set and probe set The number of testing frames for each subject is 300, Conclusions Journal ofScience & Technology 101 (2014) 159-163 using others information source for example the depth information from Kinect device or by combining with other human identification method such as RFID Moreover, Ihe hardware circuit for door opening control should be developed into a complete product like an automatic lock Acknowledgments The research leading to this paper was supported by the National Project B2013.01.41 "Study and develop an abnormal event recognition system based on compuler vision techniques" References [1] Ojala, T, M Pietikainen, and M Maenpaa, Multiresolution gray-scale and rotation invariant texmre classification width local bmary pattems IEEE Transactions on Pattern Analysis and Machine Intelligence, 24 (2002) 971-^987, [2] Ll, v., Z Ou, and G Wang, Face Recognition Using Gabor Features and Support Vector Machines, in Advances in Natural Computation, L Wang, K Chen, and Y, Ong, Editors, Spnnger Berlin Heidelberg (2005)119-122 [3] Da-Rui, S and W Le-nan A local-lo-hoIisUc face recognition approach using elastic graph matching Machine Learning and Cybernetics, 2002, Proceedmgs 2002 International Conference on, 2002, [4] Al-Sahaf, H„ Z Mengjie, and M, Johnston Binary image classification using genetic progranuning based on local binary pattems, Image and Vision Computing New Zealand (IVI^Z), 2013 28lh Intemalionai Conference of 2013[5] Viola, P and M, Jones, Robust Real-time Object Detection, Second Intemalionai workshop on statistical and computational theories of vision-modeling, learning compuHng and sampling 2001 Canada [6] ACHARYA T and A K RAY Image ProcessingPnnciples and Applications 2005: Wiley InlerScience [7] Dl, H,, el al, 1-ocai Binary Patterns and Its Application to Facial Image Analysis A Survey, Systems, Man, and Cybernetics, Part C Applications and Reviews, IEEE Transactions on, 41 (2011)765-781, [8] Ahonen, T„ A Hadid, and M, Pietikainen, Face Recognition with Local Binary Patlems, ui Computer Vision - ECCV 2004, T Pajdia and J Malas, Editors Springer Berhn Heidelberg (2004)469^81
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