International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
DOI:10.5121/ijcsa.2014.4304 ...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
40
cost and time. The founda...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
41
(1)
A data set may have m...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
42
medoid m gets replaced by...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
43
8. Pre-multiply both side...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
44
III. Tj currently belongs...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
45
(18)
3. Mean image µ of a...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
46
the effect of such a swap...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
47
21. Obtain the minimum va...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
48
Figure 1.Comparison of th...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
49
5. CONCLUSIONS AND FUTURE...
International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014
50
[4] W.Zhao, R. Chellappa&...
Upcoming SlideShare
Loading in …5
×

Robust face recognition by applying partitioning around medoids over eigen faces and fisher faces

248 views

Published on

An unsupervised learning methodology for robust face recognition is proposed for enhancing invariance to
various changes in the face. The area of face recognition in spite of being the most unobtrusive biometric
modality of all has encountered challenges with high performance in uncontrolled environment owing to
frequently occurring, unavoidable variations in the face. These changes may be due to noise, outliers,
changing expressions, emotions, pose, illumination, facial distractions like makeup, spectacles, hair growth
etc. Methods for dealing with these variations have been developed in the past with different success.
However the cost and time efficiency play a crucial role in implementing any methodology in real world.
This paper presents a method to integrate the technique of Partitioning Around Medoids with Eigen Faces
and Fisher Faces to improve the efficiency of face recognition considerably. The system so designed has
higher resistance towards the impact of various changes in the face and performs well in terms of success
rate, cost involved and time complexity. The methodology can therefore be used in developing highly robust
face recognition systems for real time environment.

Published in: Technology, News & Politics
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
248
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
3
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Robust face recognition by applying partitioning around medoids over eigen faces and fisher faces

  1. 1. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 DOI:10.5121/ijcsa.2014.4304 39 ROBUST FACE RECOGNITION BY APPLYING PARTITIONING AROUND MEDOIDS OVER EIGEN FACES AND FISHER FACES Aruna Bhat1 1 Department of Electrical Engineering, IIT Delhi, HauzKhas, New Delhi ABSTRACT An unsupervised learning methodology for robust face recognition is proposed for enhancing invariance to various changes in the face. The area of face recognition in spite of being the most unobtrusive biometric modality of all has encountered challenges with high performance in uncontrolled environment owing to frequently occurring, unavoidable variations in the face. These changes may be due to noise, outliers, changing expressions, emotions, pose, illumination, facial distractions like makeup, spectacles, hair growth etc. Methods for dealing with these variations have been developed in the past with different success. However the cost and time efficiency play a crucial role in implementing any methodology in real world. This paper presents a method to integrate the technique of Partitioning Around Medoids with Eigen Faces and Fisher Faces to improve the efficiency of face recognition considerably. The system so designed has higher resistance towards the impact of various changes in the face and performs well in terms of success rate, cost involved and time complexity. The methodology can therefore be used in developing highly robust face recognition systems for real time environment. Keywords MEDOID, PARTITIONING AROUND MEDOIDS, K-MEDOIDS, EIGEN FACE, FISHER FACE 1. INTRODUCTION Facial biometric technology has been gaining popularity over the last decade. Given still or video images of a scene, face recognition system identifies or verifies one or more persons in it using a stored database of faces. A major advantage of using face as a physiological characteristic for recognition lies in the non-invasiveness of a face recognition system. There is no need of using costly scanners for recording the data. Face recognition also reduces the time and effort spent on gathering the images because it is one of the least intrusive biometric technologies that can also be used without knowledge of a person and thus can be of immense help in developing real time surveillance applications. Face is one of the most obvious and natural means of individual identification in humans. The capability of the human brain to recognize a face out of the many faces learnt during the lifetime, in spite of variations owing to ageing, expressions, emotions, pose, light, make-up and other visual conditions or occlusions is highly remarkable. The area of Face Recognition in biometrics is centred on the development of such systems which can recognise a person based on the facial characteristics and achieve the highest possible robustness against the variations owing to aforementioned factors. Various methodologies for designing a face recognition system have been proposed. However most of them are either not invariant enough to changes in the face or incur high implementation
  2. 2. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 40 cost and time. The foundation work in developing strong face recognition systems was proposed by M.A.Turk&A.P.Pentland [1] [2]. They used Principal component Analysis (PCA) [3] for conversion of face images into Eigen faces which represent the principal components as Eigen vectors. Another method which was an improved version of [1] used Fisher Linear Discriminant Analysis [4] and generated Fisher faces [5] for recognition. Many variations of the methodology have been developed since then like Kernel Principal Component Analysis, Independent Component Analysis [6] etc. Unsupervised learning methods have also been extensively studied for their application in designing face recognition systems. A modified version of K-Means Algorithm was proposed by Mu-Chun Su and Chien-Hsing Chou [7] for face recognition. Cifarelli, Manfredi and Nieddu [8] designed a statistical face recognition system by using K- Means iterative algorithm. K-Means algorithm is also used for recognising variations in faces like expression and emotions [9]. Lu J, Yuan X and Yahagi T [10] developed a face recognition technique using Fuzzy C-Means clustering and sub-NNs. Unsupervised learning methods have been applied in designing real time 3D dynamic face recognition systems [11] [12]. Various other methodologies have been proposed for designing efficient face recognition systems with varying success rates [13]. However these techniques need to be improved and new methods need to be developed in order to achieve a high degree of robustness in face recognition systems so as to make such an unobtrusive biometric method usable in practicality. The changes in expressions also create a lot of variations in the face which makes it very hard to recognize the faces accurately. Extensive work has been done in this area as well [14] [15]. P. Ekman and E. e. Rosenberg proposed the facial action coding system (FACS) [16] which captured the variations of face features under facial expressions from a single image frame. A total of forty six Action Units (AUs) were defined in this system, which comprised of twelve AUs for upper face, eighteen AUs for lower face and rest being the combination of different AUs. It related the expressions to the muscles contraction in face. Zhang et al [17] proposed an approach to find features using Active Appearance Model (AAM). Vretos et al [18] also used Appearance based approach and SVM classifier with a high classification accuracy. Kuilenburg [19] used Delauny Triangulation for feature extraction and PCA based BPNN for classification. However designing an effective expression invariant face recognition system with high performance in terms of success rate, cost and time still remains a challenge till date. This paper presents a technique of combining the unsupervised learning method of Partitioning AroundMedoids [20] with Eigen Faces [1] [2] and Fisher Faces [5] for developing a robust face recognition system. The unique feature of Partitioning Around Medoidswhich is a type of K- Medoid clustering is that it is not impacted by the presence of outliers or noise and other extremes in the data. Therefor it enhances the overall invariance of such a system to various changes and aberration in the facial image and makes the system more robust and usable. It is evaluated for its performance in designing an expression invariant face recognition system through this paper. The variations in expressions cause significant change in the frontal face and the methodology discussed in this paper aids in mitigating their impact on the performance of the face recognition system. The rest of this paper is organized as follows: Section 2 discusses the conceptual details about Partitioning Around Medoids [20]. The proposed methodology of applying this K-Medoids Clustering algorithm to face recognition has been discussed in Section 3. Experimental results are provided in Section 4. Section 5 discusses the conclusion and the future scope. 2. OVERVIEW OF METHODOLOGY Medoid is a statistic measure which used to represent that instance of the data whose average dissimilarity to all the other data items in the data set is minimal. It is based on the degree of dissimilarity of an object to all the other objects in the cluster/data set.
  3. 3. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 41 (1) A data set may have more than one medoid in it. Unlike mean, a medoid is always a member of the data and can thus be used to model the inter-dependence of the data items. Medoid represents the most centrally located data item of the data set. As medoids are less prone to getting effected by extreme values, outliers, noise and changes in the pattern, they can be used in designing robust systems. K-Medoids clustering [20] is an algorithm to partition the data set into different clusters centred on medoids rather than a conventional mean/centroid. Therefore the medoids which are the components of the data set itself are used to represent the clusters. K-Medoids clustering begins with randomly selecting k data items as initial medoids to represent the k clusters. All the remaining items are included in that particular cluster which has its medoid closest to them. In the next iteration, a new medoid is determined which can represent the cluster better. The remaining data items are yet again assigned to the clusters having the closest medoid. In every iteration, the medoids keep changing. The method minimizes the sum of the dissimilarities between each data item and its corresponding medoid. The cycle goes on till the algorithm converges such that no medoid changes its placement. This yields the final clusters centred around their medoid with all the data members placed in the appropriate cluster based on the nearest medoid. K-Medoid Clustering Algorithm: 1. Randomly choose k data items as the initial medoids. 2. Assign each of the remaining data items to a cluster with the nearest medoid. 3. Randomly select a non-medoid data item and compute the total cost of swapping old medoid data item with the currently selected non-medoid data item. 4. For each pair of non-medoid item xi and selected medoid mk, calculate the total swapping cost C (xi mk).If C < 0, replace mkby xi. Assign each data item to the cluster with most similar representative item i.e. medoid. Therefore iif the total cost of swapping happens to be less than zero, then a swap operation is performed in order to generate the new set of k-medoids. 5. Repeat steps 2, 3 and 4 till the medoids stabilize their locations and there is no change in the medoids such that we have attain a set of k clusters that minimizes the sum of the dissimilarities of all the objects to their nearest medoids. ZD = ∑k i-1 ∑ |x-mi| (2) where, Z: Sum of absolute error for all items in the data set D: the data set containing n items k: number of clusters x: the data point in the space representing a data item mi: is the medoid of cluster Ci Various techniques of implementing K-Medoid Clustering have been developed. Partitioning Around Medoids was proposed in 1987 by Kaufman and Rousseeuw [20]. It starts from an initial set of medoids and iteratively replaces each one of the medoids by a non-medoid data item if doing so improves the total distance of the resultant clustering. It selects a set of k representative medoid data items randomly and calculates the total swapping cost S for each pair of non-medoid data item x and the selected medoid m. If the cost comes out to be a negative number then the old
  4. 4. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 42 medoid m gets replaced by x. For the newly selected medoids, each one of the remaining data items is assigned to cluster based on the most similar representative medoid. This process continues till the medoids stop changing their locations. CLARA (CLusteringLARge Applications) [21] was proposed by Kaufmann &Rousseeuw in 1990. It draws multiple samples of the data set and then applies Partitioning Around Medoidson each sample giving a better resultant clustering. It is able to deal more efficiently with larger data sets than Partitioning Around Medoids method. CLARA applies sampling approach to handle large data sets. Rather than finding medoids for the entire data set D, CLARA first draws a small sample from the data set and then applies the Partitioning Around Medoids algorithm to generate an optimal set of medoids for the sample. The quality of resulting medoids is measured by the average dissimilarity between every item in the entire data space D and the medoid of its cluster. The cost function is defined as follows: Cost(md,D) = ∑n i-1d(xi, rpst(md, xi) / n (3) where, md is a set of selected medoids, d(a, b) is the dissimilarity between items a and b and the fucnctionrpst(md, xi) returns a medoid in md which is closest to xi.The sampling and clustering processes are repeated a pre-defined number of times. The clustering that yields the set of medoids with the minimal cost is selected. Another method of performing K-Medoids clustering is CLARANS (Randomized CLARA) that was designed by Ng & Han [22]. CLARANS draws sample of neighbours dynamically. This clustering technique is similar to the graph search problem where every node is a potential solution. If the local optimum is found, search for a new local optimum is done with new randomly selected node. It is more efficient and scalable than both Partitioning Around Medoids and CLARA. 3. PROPOSED METHODOLOGY 3.1. Partitioning Around Medoids over Eigen Faces Compute the Eigen faces for the data set and apply Partitioning Around Medoids algorithm for performing K-Medoids clustering of the images as discussed below: 1. Let M be the number of images in the data set, evaluate face images Ii (i = 1,2...M) such that they are all of the same size and transform all the images I1…..i to a column vector J1….i 2. Findthe mean face m = ∑ (J ) (4) 3. Normalise the face images Ni = Ji – m (5) 4. Calculate the covariance matrix Cov = N N = AAT (6) 5. Find the eigenvectors for the matrix AT A of size m x m. 6. If v is a nonzero vector and λ is a number such that Av = λv, then v is said to be an eigenvector of A with eigenvalue λ. 7. Consider the eigenvectors vi of AT A: AT Avi = µivi (7)
  5. 5. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 43 8. Pre-multiply both sides by A such that, AAT (Avi) = µi(Avi) (8) 9. The eigenvectors of covariance matrix are ui = Avi (9) 10. Face space is given by Fi = uT Ni (10) 11. Find the medoid Ok of each class c over the set N , for P = 1 to c, where c is no. of clusters representing the individuals using Partitioning Around Medoids as explained below: 11a. The number of clusters is decided by the number of classes we have in the data set i.e. the number of individuals whose face images are present in the data set. 11b. To find the k medoids from the feature data space D, Partitioning Around Medoids begins with an arbitrary selection of kobjects. It is followed by a swap between a selected object Ts and a non-selected object Tn, if and only if this swap would result in an improvement of the quality of the clustering. To measure the effect of such a swap between Ts and Tn, the algorithmcomputes costs TCostjsn for all non-selected objects Tj. Let d(a,b) represent the dissimilarity between items a and b.The cost TCostjsnis determined as follows depending on which of the cases Tj is in: I. Tj currently belongs to the cluster represented by Ci and Tj is more similar to Tj’ than Tn. d(Tj, Tn)>=d(Tj, Tj’) whereTj’ is the second most similar medoid to Tj. So if Ts is replaced by Tn as a medoid, Tj would belong to the cluster which is represented by Tj’. The cost of the swap is: TCostjsn = d(Tj, Tj’) – d(Tj, Ts) (11) This always gives a non-negative TCostjsn indicating that there is a non- negative cost incurred in replacing Ts with Tn. II. Tj currently belongs to the cluster represented by Ci and Tj is less similar to Tj’ than Tn, d(Tj,Tn)<d(Tj, Tj’) So if Ts is replaced by Tn as a medoid, Tj would belong to the cluster represented by Tn. Thus, the cost for Tj is: TCostjsn = d(Tj’, Tn) – d(Tj, Ts) (12) Here, TCostjsncan be positive or negative, based on whether Tj is more similar to Ts or to Tn.
  6. 6. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 44 III. Tj currently belongs to a cluster other than the one represented by Ts and Tj is more similar to Tj’ than Tn, Let Tj’be the medoid of that cluster. Then even if Ts is replaced by Tn, Tj would stay in the cluster represented by Tj’. Thus, the cost is zero: TCostjsn = 0 (13) IV. Tj currently belongs to a cluster represented by Tj’ and Tj is less similar to Tj’than Tn. Replacing Ts with Tn would cause Tn to shift to the cluster of Tn from that of Tj’. Thus the cost involved is: TCostjsn = d(Tj, Tn) – d(Tj, Tj’) (14) This cost is always negative. Combining the above four cases, the total cost for replacing Ts with Tn is given by: TotalCostjsn =TCostjsn (15) 12. Compute the updated medoid Pk with the test image for each of the classes. 13. Find the difference between the new and old medoid for each class Dk = |Pk - Ok|, for k = 1 to C (16) 14. Obtain the minimum value of the difference Dmin among all Dk Dmin= Min (Dk), for k = 1 to C (17) 15. If Dmin<= t (threshold) then Ntestϵ Ii in the cluster of the subject k contributing to Dmin. 16. Repeat till medoids stabilise/converge for all medoid items Ts for all non medoid items Tn calculate the cost of swapping TCostjsn end end Select s_min and n_min such that TCosts_min,n_min = Min TCostjsn ifTCosts_min,n_min< 0, marks_min as non medoid and n_min as medoid item. End 17. All the face images are assigned to clusters that represent the face of an individual subject based on the extent of similarity to the medoid data item of that cluster. 3.2. Partitioning Around MedoidsoverFisher Faces Compute the Fisher faces for the data set and apply Partitioning Around Medoids algorithm for performing K-Medoids clustering of the images as discussed below: 1. X1, X2, …,Xc represent the c classes of face in the database. Each face class is represented by vector Xi, i = 1, 2, …, c has k facial images xj, j=1, 2, …, k. 2. Find the mean image µi of each class Xi as:
  7. 7. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 45 (18) 3. Mean image µ of all the face classes in the database can be calculated as: (19) 4. Calculate the within-class scatter matrix: (20) 5. Calculate the between-class scatter matrix: (21) 6. We find the projection directions as the matrix W that maximizes Ẑ = argmaxJ(Z) = |ZT SBZ| / | ZT SWZ | (22) 7. We can observe that it represents a generalized eigenvalue problem where the columns of Z are given by the vectors Zi such that, SBZi = λiSWZi (23) 5. Find the product of SW -1 and SBand calculate its eigenvectors after reducing the dimension of the feature space. 6. Compose a matrix Z that represents all eigenvectors of SW -1 * SB by placing each eigenvector Zi as a column in Z. 8. Each face image xj∈ Xi can be projected into this face space as: Fi = ZT (xj – µ) (24) 18. Find the medoid Ok of each class c over the set N , for P = 1 to c, where c is no. of clusters representing the individuals using Partitioning Around Medoids as explained below: 11a. The number of clusters is decided by the number of classes we have in the data set i.e. the number of individuals whose face images are present in the data set. 11b. To find the k medoids from the feature data space D, Partitioning Around Medoids begins with an arbitrary selection of kobjects. It is followed by a swap between a selected object Ts and a non-selected object Tn, if and only if this swap would result in an improvement of the quality of the clustering. To measure T ii c i iB NS ))(( 1  −−= ∑= ∑= = c i i c 1 1  T ik c i Xx ikW xxS ik )()( 1  −−= ∑ ∑= ∈
  8. 8. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 46 the effect of such a swap between Ts and Tn, the algorithm computes costs TCostjsn for all non-selected objects Tj. Let d(a,b) represent the dissimilarity between items a and b.The cost TCostjsnis determined as follows depending on which of the cases Tj is in: I. Tj currently belongs to the cluster represented by Ci and Tj is more similar to Tj’ than Tn. d(Tj, Tn)>=d(Tj, Tj’) whereTj’ is the second most similar medoid to Tj. So if Ts is replaced by Tn as a medoid, Tj would belong to the cluster which is represented by Tj’. The cost of the swap is: TCostjsn = d(Tj, Tj’) – d(Tj, Ts) (25) This always gives a non-negative TCostjsn indicating that there is a non- negative cost incurred in replacing Ts with Tn. II. Tj currently belongs to the cluster represented by Ci and Tj is less similar to Tj’ than Tn, d(Tj,Tn)<d(Tj, Tj’) So if Ts is replaced by Tn as a medoid, Tj would belong to the cluster represented by Tn. Thus, the cost for Tj is: TCostjsn = d(Tj’, Tn) – d(Tj, Ts) (26) Here, TCostjsncan be positive or negative, based on whether Tj is more similar to Ts or to Tn. III. Tj currently belongs to a cluster other than the one represented by Ts and Tj is more similar to Tj’ than Tn, Let Tj’be the medoid of that cluster. Then even if Ts is replaced by Tn, Tj would stay in the cluster represented by Tj’. Thus, the cost is zero: TCostjsn = 0 (27) IV. Tj currently belongs to a cluster represented by Tj’ and Tj is less similar to Tj’than Tn. Replacing Ts with Tn would cause Tn to shift to the cluster of Tn from that of Tj’. Thus the cost involved is: TCostjsn = d(Tj, Tn) – d(Tj, Tj’) (28) This cost is always negative. Combining the above four cases, the total cost for replacing Ts with Tn is given by: TotalCostjsn =TCostjsn (29) 19. Compute the updated medoid Pk with the test image for each of the classes. 20. Find the difference between the new and old medoid for each class Dk = |Pk - Ok|, for k = 1 to C (30)
  9. 9. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 47 21. Obtain the minimum value of the difference Dmin among all Dk Dmin= Min (Dk), for k = 1 to C (31) 22. If Dmin<= t (threshold) then Ntestϵ Ii in the cluster of the subject k contributing to Dmin. 23. Repeat till medoids stabilise/converge for all medoid items Ts for all non medoid items Tn calculate the cost of swapping TCostjsn end end Select s_min and n_min such that TCosts_min,n_min = Min TCostjsn ifTCosts_min,n_min< 0, marks_min as non medoid and n_min as medoid item. End 24. All the face images are assigned to clusters that represent the face of an individual subject based on the extent of similarity to the medoid data item of that cluster. The resultant clustering classifies the face images of subjects with varying degrees of expressions to their correct individual classes. 4. EXPERIMENTAL RESULTS Open source data sets like JAFFE database [25] have been used to evaluate the performance of the proposed methodology. JAFFE database contains various face images of different subjects with varying expressions and emotions. The face images were segmented and pre-processed following which Eigen Face and Fisher Face algorithms were applied. Clustering of the data set thus retrieved was done using Partitioning Around Medoids technique. It was found to be effective in classifying the facial images correctly even in the presence of variations in the images due to the presence of outliers, noise and changes in expressions and emotions. The results were compared to the methods of Eigen Face (PCA), Fisher face (FLDA), Kernel PCA (KPCA), Independent Component Analysis (ICA) and Topography invariant ICA (TICA). However when Partitioning Around Medoids was used in combination with Eigen Faces and Fisher Faces,the results obtained were significantly better and displayed much higher success rates with increased robustness towards variations in expressions. Applying Partitioning Around Medoids with Eigen Face and Fisher Face technique depicted an increase in the recognition accuracy. Therefore using this technique of K-Medoid increased the overall invariance of the face recognition system and made it sturdierin spite of the presence of noise, outliers and variations in expressions in the face as shown below:
  10. 10. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 48 Figure 1.Comparison of the Performance of Eigen Face, Fisher Face, KPCA, ICA, TICAvs. Partition Around Medoids with Eigen Face and Partition Around Medoids Fisher Face Figure 2.Recognition Performance of Partition Around Medoids with Eigen Face and Partition Around Medoids Fisher Face with different emotions (JAFFE Database [23] 6 emotions + 1 Neutral) 0 10 20 30 40 50 60 70 80 90 100 1 Eigen Face Fisher Face KPCA ICA TICA Partitioning Around Medoids over Eigen Faces 80 82 84 86 88 90 92 94 96 1 Anger Disgust Fear Happiness Sadness Surprise Neutral
  11. 11. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 49 5. CONCLUSIONS AND FUTURE SCOPE Due to unique properties of outlier and noise insensitivity of K-Medoids clustering a methodology to design a robust expression invariant face recognition system has been proposed and evaluated in this paper. It was observed that the efficiency of the system increase manifold by integrating Partitioning Around Medoids with the standard face recognition techniques. There was a remarkable increase in performance of face recognition by applying Partitioning Around Medoidsover the data set retrieved after Eigen face and Fisher face spaces were identified for the input data set. The success rate was significantly higher as compared to running Eigen Face (PCA), Fisher face (FLDA), Kernel PCA (KPCA), Independent Component Analysis (ICA) and Topography invariant ICA (TICA) techniques separately. Since Partitioning Around Medoidsboosted the standard techniques of Eigen Face and Fisher Face, it leads us towards its application in designing stronger face recognition systems. The clustering algorithm can also be integrated with other standard face recognition techniques and evaluated for its performance even in the presence of various other kinds of variations in the frontal image of the face like the changes in expressions, emotions etc. Partitioning Around Medoids may be replaced with other K-Medoid clustering algorithms like CLARA and CLARANS and tested for bettertime efficiency and performance. CLARA has been observed to be more effective while dealing with the larger data sets as compared to PAM. However its efficiency depends on the sample size. A good clustering based on samples will not necessarily represent a good clustering of the whole data set if the sample is biased. Due to this the whole idea of bringing in invariance to the face recognition system may get adversely impacted. On the other hand CLARANS is more efficient and scalable than both PAM and CLARA. If the local optimum is found, CLARANS starts with new randomly selected node in search for a new local optimum. It may also be appropriately studied for how it can contribute towards designing face recognition applications that are invariant to various other kinds of changes like variations in pose, illumination, facial distractions like makeup and hair growth etc. as facial variations are not limited to changes in expressions and emotions only. Therefore the methodology needs to be studied further for its application development of techniques to make a stronger face recognition system with even better performance in terms of success rate and efficiency with minimum cost and time involved. ACKNOWLEDGEMENTS The author would like to thank the faculty and the staff at Electrical Engineering Department,Indian Institute of Technology, Delhi for theirvalued guidance and help they have graciously provided for the research work. REFERENCES [1] M.Turk&A.Pentland, (1991) “Eigenfaces for Recognition”, Journal of Cognitive Neuroscience, vol.3, no.1, pp. 71-86, 1991a. [2] M. Turk & A. Pentland, (1991) “Face Recognition Using Eigenfaces”, Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586-591. [3] P.J.B. Hancock, A.M. Burton & V. Bruce, (1996) “Face processing: Human perception and principal components analysis”, Memory and Cognition, 24(1):26-40.
  12. 12. International Journal on Computational Sciences & Applications (IJCSA) Vol.4, No.3, June 2014 50 [4] W.Zhao, R. Chellappa& A. Krishnaswamy, (1998) “Discriminant Analysis of Principal Components for face Recognition”, Proc. Third Int’l Conf. Automatic Face and Gesture Recognition, pp. 336-341. [5] P.N. Belhumeur, J.P. Hespanha& D.J. Kriegman, (July 1997) “Eigenfaces vs. Fisherfaces: Recognition Using Class-Specific Linear Projection”, PAMI(19), No. 7, pp. 711-720. [6] Marian Stewart Bartlelt, Javier R. Movellan& and Ferrence J. Sejnowski, (Nov’2002) “Face Recognition by Independent Component Analysis”, IEEE Trans. on Neural Networks, vol.13, no. 6. [7] Mu-Chun Su and Chien-Hsing Chou, (June 2001)A Modified Version of the K-Means Algorithm with a Distance Based on Cluster Symmetry, IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 23, No. 6. [8] Cifarelli, C. Manfredi, G. Nieddu, L. (2008), "Statistical Face Recognition via a k-Means Iterative Algorithm", Machine Learning and Applications, ICMLA '08. [9] Zeki, A.M., btSerda Ali, R., Appalasamy, P. (2012), "K-means approach to facial expressions recognition", Information Technology and e-Services (ICITeS). [10] Lu J, Yuan X, Yahagi T., (January 2007) A method of face recognition based on fuzzy c-means clustering and associated sub-NNs, IEEE Trans Neural Netw,18(1):150-60. [11] MariosKyperountasa, Author Vitae, AnastasiosTefasa, Author Vitae, Ioannis Pitas, (March 2008) Dynamic training using multistage clustering for face recognition Pattern Recognition , Elsevier, Volume 41, Issue 3, Pages 894–905. [12] Zheng Zhang, Zheng Zhao, Gang Bai, (2009) 3D Representative Face and Clustering Based Illumination Estimation for Face Recognition and Expression Recognition, Advances in Neural Networks – ISNN 2009 Lecture Notes in Computer Science Volume 5553, pp 416-422. [13] W. Zhao, R. Chellappa, A. Rosenfeld, P.J. Phillips, “Face Recognition: A Literature Survey”. [14] Rothkrantz M., “Automatic Analysis of Facial Expressions: The State of the Art,” IEEE Transaction Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1424-1445, 2000. [15] Fasel B. and Luettin J., “Automatic Facial Expression Analysis: a Survey,” IEEE Pattern Recognition, vol. 36, no. 1, pp. 259-275, 2003. [16] P. Ekman and E. e. Rosenberg. The facial action coding system: A technique for the measurement of facial movement. Consulting Psychologists press, 1978. [17] Zhang Y. and Martinez A., “Recognition of Expression Variant Faces Using Weighted Subspaces,” in Proceedings of the 17th International Conference on Pattern Recognition, vol. 3, pp. 149-152, 2004. [18] Vretos N., Nikolaidis N., and Pitas I., “A Model-Based Facial Expression Recognition Algorithm using Principal Component Analysis,” in Proceedings of the 16th IEEE International Conference on image Processing, Cairo, pp.3301-3304, 2009. [19] Kuilenburg H., Wiering M., and Uyl M., “A Model Based Method for Automatic Facial Expression Recognition,” Springer Verlag Proceedings of the ECML, vol. 54, pp. 194-205, 2005. [20] Kaufman, L. and Rousseeuw, P.J. (1987), Clustering by means of Medoids, in Statistical Data Analysis Based on the L1 - Norm and Related Methods, edited by Y. Dodge, North- Holland, 405– 416. [21] Kaufman, L. and Rousseeuw, P. J. (2005). Finding groups in data: An introduction to cluster analysis. John Wiley and Sons. [22] Ng, R.T., Jiawei Han,(2002) "CLARANS: a method for clustering objects for spatial data mining", Knowledge and Data Engineering, IEEE Transactions on (Volume:14, Issue:5), Pg 1003 - 1016, ISSN: 1041-4347. [23] JAFFE database, Coding Facial Expressions with Gabor Wavelets Michael J. Lyons, Shigeru Akamatsu, Miyuki Kamachi&JiroGyoba Proceedings, Third IEEE International Conference on Automatic Face and Gesture Recognition, April 14-16 1998, Nara Japan, IEEE Computer Society, pp. 200-205. Authors The author is a research scholar at Indian Institute of Technology, HauzKhas, Delhi.

×