Analisis keputusan Sijil Pelajaran Malaysia (SPM) 2011 menunjukkan penurunan 

pencapaian bagi Sekolah Menengah Vokasional. Oleh itu, kajian ini dilaksanakan 

bertujuan untuk mengkaji hubungan di antara gaya pembelajaran dengan pencapaian 

akademik pelajar. Kajian ini juga ingin mengenalpasti gaya pembelajaran paling 

dominan yang diamalkan oleh pelajar serta melihat perbezaan gaya pembelajaran 

dengan jantina pelajar. Seramai 131 orang Pelajar Tingkatan Empat Kursus 

Vokasional Di Sekolah Menengah Vokasional Segamat di Johor telah terlibat dalam 

kajian ini. Soal selidik Index of Learning Style (ILS) yang dibangunkan oleh Felder 

dan Silverman (1991) yang mengandungi 44 soalan telah digunakan untukh 

menjalankan kajian ini. Gaya pembelajaran pelajar dapat dilihat melalui empat 

dimensi gaya pembelajaran yang terdiri dari dua sub-skala yang bertentangan iaitu 

dimensi pelajar Aktif dan Reflektif, dimensi pelajar Konkrit dan Intuitif, dimensi 

pelajar Verbal dan Visual, serta dimensi pelajar Tersusun dan Global. Data yang 

diperolehi dianalisis dengan menggunakan perisian Statistical Package for Social 

Science for WINDOW release 20.0 (SPSS.20.0). Ujian Korelasi Pearson digunakan 

untuk menganalisis data dalam mengkaji hubungan gaya pembelajaran dengan 

pencapaian akademik pelajar. Nilai pekali p yang diperolehi di antara gaya

pembelajaran dengan pencapaian pelajar adalah (p=0.1 hingga 0.4). Ini menunjukkan 

tidak terdapat hubungan yang signifikan di antara dua pembolehubah tersebut. Kajian 

ini juga mendapati bahawa gaya pembelajaran yang menjadi amalan pelajar ialah 

gaya pembelajaran Tersusun. Hasil kajian juga mendapati bahawa tidak terdapat 

perbezaan yang signifikan di antara gaya pembelajaran dengan jantina pelajar

Anwar, Nurul Farahanaa

English

UTHM Institutional Repository

2010 Asia Pacific Conference on Circuits and Systems (APCCAS 2010)6 - 9 December 2010, Kuala Lumpur, MalaysiaOptimal Discrete Wavelet Transform (DWT)Features for Face RecognitionPaul NichollSchool of Electronics, ElectricalEngineering & Computer ScienceQueen’s Univ., Northern IrelandEmail: p.nicholl@qub.ac.ukAfandi AhmadJEC, Faculty of. Elec. and Electronic Eng.Univ. Tun Hussein Onn MalaysiaJohor, MalaysiaEmail: afandia@uthm.edu.myAbbes AmiraNIBEC, Faculty of Comp. and Eng.Univ. of Ulster, Jordanstown CampusNorthern IrelandEmail: a.amira@ulster.ac.ukAbstract—Face recognition systems usually include pre-processing, in order to crop the training and probe images.This often involves arbitrarily-chosen segmentation boundaries,which may exclude discriminative face information or includeirrelevant pixels corresponding to background, hair, etc.The work presented in this paper creates a rich featurevector using discrete wavelet transform (DWT) coefficients,which is then optimized to exclude useless information. Thisoptimization process eliminates the need to overly crop images,as background will be automatically excluded. Experiments onthe AT&T database show that the technique improves resultssignificantly, with recognition rates increasing from 93% to97.5% when using the Haar wavelet.Index Terms—Face recognition, multiresolution, statistical,wavelet.I. INTRODUCTIONFace recognition has become the subject of intense interestin recent years. Biometric techniques such as fingerprint andiris recognition have a high degree of accuracy; however, theyrequire the subject to comply with the recognition process.Face recognition can be performed with a lesser degree ofcompliance, meaning that people can be identified withouttheir consent or even their knowledge [1].A multitude of techniques have been applied to facerecognition. Many of these techniques fall into the templatematching category, where faces are treated as two-dimensionalarrays of intensity values. These include principal componentanalysis (PCA), where the variance among a set of faceimages is represented by a number of eigenfaces [2], andindependent component analysis (ICA) [3], where facesare assumed to be linear mixtures of some unknown latentvariables. In neural network models (NNMs), the system issupplied with a set of training images along with correctclassification, thus allowing the neural network to ascertain aweighting system to determine which areas of an image aremost important [4]. In hidden Markov models (HMMs) [5],a statistical model characterizes each identity in the trainingdatabase.Hybrid multiresolution approaches have received muchattention in recent years. The discrete wavelet transform(DWT) has been used along with a number of techniques,including PCA [6], ICA [7] and support vector machines(SVM) [8]. DWT provides the advantage of extracting datathat is useful for recognition and discarding that which is not.In face recognition applications employing DWT, the faceimage is often decomposed to a specific scale, with the featurevector being created from all the low-low (LL) coefficients atthat scale [9]. An alternative approach is to extract featuresfrom other quadrants at a variety of scales. The coefficientsfrom quadrants other than LL are not, by themselves, suitablefor direct comparison, so statistics such as the L2-norm aresometimes calculated and used as features [6]. These twoapproaches produce similar results, using feature sets that donot intersect.In addition, those designing face recognition systems haveto decide which areas of face images contribute towardsclassification and which detract from it (for example, imagebackground or hair). The segmentation approach oftenemploys arbitrary segmentation boundaries, which may besuboptimal.The work described in this paper aims to automaticallyselect the DWT coefficients which contribute most toclassification, using both LL and non-LL quadrant features. Itthen optimizes the feature vector, choosing only the featuresthat are most discriminative. This eliminates the need tooverly crop the face images.The remainder of this paper is organized as follows.In Section II, the mathematical background is explained.Section III describes the recognition approach used.Experimental results and analysis are presented in Section IV.Section V contains concluding remarks.II. MATHEMATICAL BACKGROUNDA. Discrete Wavelet Transform (DWT)DWT allows a signal to be localized in both time andfrequency. It operates by convolving a target function withwavelet kernels, in order to obtain wavelet coefficientsrepresenting the contributions of wavelets in the function atdifferent scales and orientations. DWT can be mathematicallyexpressed by Equation 1.DWTx(n)={dj,k =∑x(n)h∗j (n− 2jk)aj,k =∑x(n)g∗j (n− 2jk) (1)978-1-4244-7456-4/10/$26.00 ©2010 IEEE 1322010 Asia Pacific Conference on Circuits and Systems (APCCAS 2010)6 - 9 December 2010, Kuala Lumpur, MalaysiaThe coefficients dj,k refer to the detail components insignal x(n) and correspond to the wavelet function, whereasaj,k refer to the approximation components in the signal.The functions h(n) and g(n) in the equation represent thecoefficients of the high-pass and low-pass filters respectively,whilst parameters j and k refer to wavelet scale and translationfactors. For the case of images, the one-dimensional (1-D)DWT can be extended to two dimensions.Figure 1 illustrates the effect of applying the non-standardwavelet transform to an image from the AT&T Database ofFaces [10].(a) (b) (c)Fig. 1. Wavelet transform of image (a) Original Image (b) 1-levelHaar decomposition (c) Complete Haar decomposition.B. Principal Component Analysis (PCA)PCA is a dimensional-reduction technique. It regards data aspoints in a high-dimensional space, operating by finding a newcoordinate system for a dataset, with the axes (or principalcomponents) ordered by the degree of variance containedwithin the training data. A set of faces can be representedas points in this new coordinate system.PCA does not model relationships between neighboringpixels in an image – it analyzes each one individually.Consequently, a face image x of dimension i x j is convertedto a column vector of length N , where N = ij.x =p1,1p1,2...p1,jp2,1...pi,j−1pi,j(2)and ps,t corresponds to a pixel from the sth row and the tthcolumn. A set of M face images {xi} may be represented asa matrix X of dimension N x M , where,X = [x1x2x3...xM ] (3)The ‘average’ face is calculated and subtracted from eachface in X , giving X ′,X ′ = [(x1 − x)(x2 − x)(x3 − x)...(xM − x)] (4)The principal components of this set are found bycalculating the eigenvectors of the covariance matrix C,where,C =M∑i=1X ′X ′T (5)The calculated eigenvectors are used as an orthogonal basisto represent the training set faces. The k-eigenvectors with thehighest associated eigenvalues represent the greatest variancewithin the face set and are retained for representation. Eachface image is then projected onto the selected eigenvectors,producing a k-dimensional feature vector.III. RECOGNITION APPROACHThe recognition approach is based on standard DWT/PCAface recognition. Figure 2 provides a general overview ofthe system. Features are initially extracted using multiscalevector creation (MVC). The vector produced then undergoesmultiscale vector optimization (MVO). The final featurevectors are classified using PCA.PCACoefficientSelectionCoefficient vectorCompare with face databaseRecognition resultsFace DatabaseDWTCoefficientSelectionFig. 2. System overview.A. Multiscale Vector Creation (MVC)MVC involves extracting features using DWT. Each faceimage is divided into overlapping horizontal strips of height jpixels with an overlap of p pixels. Each horizontal stripis subsequently segmented vertically into blocks of widthk pixels, with an overlap of p. This is illustrated in Figure 3.For an image of width (w) and height (h), there will beapproximately (((h/(j − p)) + 1) ∗ (w/(k − p)) + 1) blocks.Each block is then decomposed using a selected waveletfilter to the xth decomposition level, chosen in advance. Thefeature set is composed of two components:• All coefficients from the LL quadrant at the xth scale;and• The L2 norm values of the low-high (LH), high-low (HL)and high-high (HH) quadrants, from scales one (1) to x.These two feature set components are individuallynormalized, so that all values fall between zero (0) andone (1).1332010 Asia Pacific Conference on Circuits and Systems (APCCAS 2010)6 - 9 December 2010, Kuala Lumpur, Malaysiap {{ pkFace image being segmented into stripsFace strip being segmented into blocksFace blockkjj  {{Fig. 3. The creation of the block sequence.B. Multiscale Vector Optimization (MVO)Coefficients extracted in the previous step are thenoptimized, with the most discriminative coefficients beingselected. The inter- and intra-class standard deviations foreach coefficient are calculated and the ratio of these values isdetermined.This ratio indicates how tightly the coefficient’s values areclustered within a class, compared to the spread within theentire training dataset. The coefficients with the highest ratiosare deemed to be the most discriminative and are retained forrecognition.IV. RESULTS AND ANALYSISA. DatasetsExperiments were carried out using the AT&T Database ofFaces [8], which contains ten greyscale images each of fortyindividuals. Five images for each individual were used forsystem training, with the other five used for testing.B. Multiscale Vector Creation (MVC)The first set of experiments was designed to evaluate theeffectiveness of MVC. The training set images were split intoblocks as shown in Figure 3. A variety of block heights andwidths were tested, and the overlap used was four.An example of the results obtained is shown in Figure 4.As can be seen from the graph, the use of Haar, 4th scale,LL quadrant coefficients, does not significantly improverecognition performance over that provided by spatial(non-DWT) recognition. Both approaches reach a maximumrecognition rate of 93%. However, the adoption of MVCproduces a significant improvement. Peak recognition rateis increased to 97.5%, which corresponds to a reduction inincorrectly-classified images of 64%.C. Multiscale Vector Optimization (MVO)The second set of experiments evaluated the effectivenessof MVO. The graph in Figure 5 represents results forMVC+MVO with a coefficient selection percentage (thepercentage of coefficients retained from the previous stage)of 57%. Other parameters were the same as for the first set ofexperiments. Although the maximum recognition rate remainsSpatialHaar, 4thscaleHaar, 4thscale, MVCEigenvectorsRecogni ti on accur acy (%)0 10 20 30 40 50 60 70 80 90 1007580859095100Fig. 4. Recognition performance for MVC.Haar, 4thscaleHaar, 4thscale, MVCHaar, 4thscale, MVC+MVOEigenvectors0 10 20 30 40 50 60 70 80 90 100Recogni ti on accur acy (%)7580859095100Fig. 5. Recognition for MVC and MVO.at 97.5%, this level is reached more quickly. The averagerecognition rate for MVC+MVO is 95.61%, compared with95.02% for MVC alone. As the number of eigenvectorsrequired for peak recognition rates cannot be pre-determined,this is an important increase. The graph also highlights thesignificant improvement that MVC+MVO provides overstandard DWT/PCA face recognition.TABLE ICOMPARATIVE RESULTS ON AT&T DATABASEMethod Accuracy (%) Refs.ICA 85 [3]DCT/HMM 84 [11]PCA 91 [12]Gabor Filters & Rank Correlation 91.5 [13]2D-PHMM 94.5 [14]TNPDP 96.5 [15]MVC+MVO 97.5 Proposed1342010 Asia Pacific Conference on Circuits and Systems (APCCAS 2010)6 - 9 December 2010, Kuala Lumpur, MalaysiaAs Table I shows, the MVC+MVO approach to facerecognition compares well with other techniques from theliterature that have used this training set.V. CONCLUSIONSA face recognition approach based on DWT and PCA hasbeen proposed. It eliminates the need to manually segmentinput images and creates a rich feature vector, based onLL quadrant DWT coefficients and L2-norm values fromother quadrants. It optimizes the feature vector based on thediscriminatory power of each coefficient. Recognition resultsshow that this approach significantly outperforms recognitionusing standard DWT/PCA recognition employing LL featuresonly.Future work will involve analyzing results for otherwavelets, as well as investigating approaches for automaticallychoosing the percentage of coefficients selected by MVO.Classification methods other than PCA will be investigatedand larger face databases will be used for testing.REFERENCES[1] W. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Facerecognition: A literature survey,” ACM Comput. Surv., vol. 35, no. 4,pp. 399–458, 2003.[2] M. Turk and A. Pentland, “Eigenfaces for recognition,” Journal ofCognitive Neuroscience, vol. 3, pp. 71–86, 1991.[3] P. C. Yuen and J. H. Lai, “Face representation using independentcomponent analysis.” Pattern Recognition, vol. 35, no. 6, pp. 1247–1257, 2002.[4] E. Kussul, T. Baidyk, and M. Kussul, “Neural network system for facerecognition.” in Circuits and Systems, 2004. ISCAS ’04. Proceedings ofthe 2004 International Symposium on, 2004, pp. 768–771.[5] T. A. H. Othman, “A separable low complexity 2D HMM withapplication to face recognition,” IEEE Trans. Pat. Anal. Mach. Intell.,vol. 25, no. 10, pp. 1229–1238, 2003.[6] G. C. Feng, P. C. Yuen, and D. Q. Dai, “Human face fecognition usingPCA on wavelet subband,” Journal of Electronic Imaging, vol. 9, pp.226–233, April 2000.[7] M. T. Harandi, M. N. Ahmadabadi, and B. N. Araabi, “Face recognitionusing reinforcement learning,” in Image Processing, 2004. ICIP ’04.2004 International Conference on, 2004, pp. 2709–2712.[8] H. Kemal Ekenel and B. Sankur, “Multiresolution face recognition,”Image and Vision Computing, vol. 23, no. 5, pp. 469–477, 2005.[9] L. Bai and L. Shen, “Combining wavelets with HMM for facerecognition.” in Proceedings of SGAI, 23rd International Conference onInnovative Techniques and Applications of Artificial Intelligence, 2003.[10] F. Samaria and A. Harter, “Parameterisation of a stochastic model forhuman face identification,” in Applications of Computer Vision, 1994.,Proceedings of the Second IEEE Workshop on, Sarasota (Florida), 5-71994, pp. 138–142.[11] A. Nefian and M. Hayes, “Hidden markov models for face recognition,”in International Conference on Acoustics, Speech, and Signal Processing(ICASSP 98), 1998, pp. 2721–2724.[12] P. Nicholl, A. Amira, and R. Perrott, “An automated grid-enabled facerecognition system using hybrid approaches,” Proceedings of the 5thIEE/IEEE Postgraduate Research Conference on Electronics, Photonics,Communications and Networks (PREP’05), pp. 144–146, 2005.[13] O. Ayinde and Y. H. Yang, “Face recognition approach based on rankcorrelation of gabor-filtered images.” Pattern Recognition, vol. 35, no. 6,pp. 1275–1289, 2002.[14] F. Samaria, “Face recognition using hidden markov models,” Ph.D.dissertation, Cambridge University Engineering Department, 1994.[15] J. Lu and Y. Tan, “Enhanced face recognition using tensor neighborhoodpreserving discriminant projections,” in 15th IEEE InternationalConference on Image Processing, 2008. ICIP 2008, 2008, pp. 1916–1919.135

Hubungan gaya pembelajaran dengan pencapaian akademik pelajar aliran vokasional

Abstract

Similar works

Full text

Available Versions

UTHM Institutional Repository