HANFIS: A New Fast and Robust Approach for Face Recognition and Facial Image Classification R.Senthilkumar Assistant Professor Department of Electronics and Communication Engineering Institute of Road and Transport Technology, Erode, Tamilnadu, India E-mail: rsenthil_1976@yahoo.com Dr.R.K.Gnanamurthy Professor and Director P.P.G. Institute of Technology Coimbatore, Tamilnadu, India E-mail: rkgnanam@yahoo.co.in International Conference on Smart Innovations in Communications and Computational Sciences -2017 (ICSICCS-2017) Venue: North West Group of Institutions, Moga, Punjab, June 23-24, 2017

Aim The purpose of this paperwork is to improve the recognition rate and reducing the recognition time required for face recognition application HANFIS-Concept The feature extraction is done using texture based Haralick feature extraction method and facial image classification is achieved by using modified ANFIS classifier. This method is simply called as HANFIS.

Face Databases Tested [1]. ORL [2]. Yale [3]. Surveillance and [4] Face Databases Tested [1]. ORL [2]. Yale [3]. Surveillance and [4]. FERET Compared with [1]. Texture based Haralick Feature Extraction and Naïve-Bayes Classifier [2]. Approach based 2dPCA Feature Extraction and k-Nearest Neighbour Classifier 2dPCA Feature Extraction and Naïve-Bayes Classifier 2dPCA Feature Extraction and SVM Classifier [3]. Documentation based Bag Of Visual Words

Block Diagrammatic Representation of Existing methods Fig. 1. Haralick feature extraction and Naïve-Bayes classifier Fig. 2. 2dPCA feature extraction and kNN classifier Fig. 3. 2dPCA feature extraction and Naïve-Bayes classifier

Fig. 4. 2dPCA feature extraction and SVM classifier Fig. 5. Bag of Visual Words diagrammatic representation

Block Diagrammatic Representation of Proposed method Fig. 6. HANFIS architecture-Block diagram representation

Haralick_feature =readexcel (‘FaceDatabase.xls’); y = Haralick_feature {1 to 12}; //trainData C = Num_of_classes; N = Numb_of_subjects; L = CxN; //Total no. of faces in entire database y = norm(y); //Normalize the features define memType; //define Fuzzy membership type define memFun; //define Fuzzy membership function IN_FIS = genfis(y,memType,memFun); [TrainFIS,TestFIS} = anfis (y,IN_FIS,epoch,testData); Out1 =evalfis([1 to 12],TrainFIS); Out2 =evalfis([1 to 12],TestFIS); MSE = sqrt(((Out1-Out2)^2))/Length(Out1)); Recog = 0; If MSE< min_MSE Recog = Recog+1; End Recog_Accuracy = (Recog/L)x100; HANFIS Algorithm- Pseudo Code

Experimental Results and Discussion Table 1, lists the recognition time required in seconds for different face recognition techniques. The recognition time for Haralick+Naïve-Bayes method calculated for three different cases: 12 features, 22 features, selected five features. The recognition time for other methods 2dPCA+kNN, 2dPCA+Naïve-Bayes and 2dPCA+SVM are calculated for three different feature sizes such as Cx1, Cx2 and Cx3. The 2dPCA+kNN technique and 2dPCA+SVM techniques require large recognition time compared to other methods. Our method is tested for three different Mean Square Errors; 1e-8, 1e-7.9 and 1e-7.5. The HANFIS method requires less than 1% recognition time compared to other methods. Table 2, listed the recognition accuracy in percentage for different face recognition methods tested for four databases. The recognition accuracy of BOVW method is comparable with HANFIS method, but it requires larger recognition time compared to the proposed method. The Haralick+Naïve-Bayes classifier is a worst classification method compared to other four methods. This method not only gives poor recognition rate but also consumes large recognition time. The 2dPCA+kNN, 2dPCA+Naïve-Bayes and 2dPCA+SVM methods produce moderate recognition accuracy. The 2dPCA+kNN requires larger recognition time for FERET and ORL face databases. This is because they are big databases compared to other face databases discussed here.

Table 1. Comparison of Recognition Time in Secs Table 1. Comparison of Recognition Time in Secs. of Different Face recognition Techniques (intel core i3 processor, 4GB RAM) Feature Extraction Method Face Databases Recognition Time in Seconds ORL (AT &T) Surv Yale FERET Texture based FR Haralick Feature Extraction + Naives-Bayes Classifier   Feature Dimension First 12 0.039 0.0132 0.016 0.109 All 22 0.042 0.0134 0.020 0.115 Selected Five (Contrast, Correlation, Energy, entropy, homogeneity) 0.038 0.0117 0.017 0.099 Appearance based FR 2dPCA kNN Classifier CxK=1 6.171 0.415 1.879 54.72 CxK=2 7.362 0.621 2.575 79.79 CxK=3 7.643 1.000 4.371 74.04 2dPCA Navies–Bayes Classifier 0.294 1.572 0.453 3.734 0.301 0.434 0.431 4.366 0.322 0.972 0.981 3.319 2dPCA SVM Classifier 4.854 1.383 4.541 35.52 7.322 0.933 2.272 33.44 7.343 0.821 3.682 33.91 Documentation based FR Bag of Visual Words Feature Dimension 50 0.269 0.266 0.4118 0.6957 + ANFIS classifier(HANFIS) (First 12) MSE 1e-8 0.049 0.0272 0.0381 0.0251 MSE 1e-7.9 0.048 0.0275 0.0376 0.0319 MSE 1e-7.5 0.043 0.0283 0.0375 0.0337

Table 2. Performance Compariosn of Recognition Accuracy in % of Different Face Recognition Techniques Feature Extraction Method Face Databases Recognition Accuracy in Percentage ORL (AT &T) Surv Yale FERET Texture based FR Haralick Feature Extraction and Navies –Bayes Classifier   Dimension First 12 63.50 30.00 62.19 38.87 All 22 65.50 24.00 54.87 36.84 Selected Five (Contrast, Correlation, energy, entropy, homogeneity) 47.00 22.00 42.68 33.19 Appearance based FR 2dPCA kNN Classifier CxK, K=1 85.00 70.00 72.00 79.04 CxK, K=2 93.00 66.00 84.00 79.79 CxK, K=3 95.50 68.00 82.66 81.06 2dPCA Navies –Bayes Classifier 79.17 42.00 68.33 73.23 90.83 86.67 88.89 92.50 78.00 91.67 2dPCA SVM Classifier 68.50 70.66 68.68 85.50 80.00 77.27 87.00 74.00 85.33 77.02 Documentation based FR Bag of Visual Words Feature Dimension 1x500 98.00 82.00 89.33 97.9798 ANFIS classifier (HANFIS) parameters MSE1 1e-08 92.25 83.00 90.3 95.75 MSE2 1e-07.9 96.75 100 98.58 MSE3 1e-07.5

Fig. a. Pie chart shows HANFIS recognition time in secs Fig. a. Pie chart shows HANFIS recognition time in secs. for ORL database Fig. b. Pie chart shows HANFIS recognition time in secs. for Surveillance database Fig. c. Pie chart shows HANFIS recognition time in secs. for Yale database

Fig. a. Comparison of HANFIS method recognition rate for ORL face database Fig. b. Comparison of HANFIS method recognition rate for Surveillance face database

Fig.c. Comparison of HANFIS method recognition accuracy for Yale database Fig.d. Comparison of HANFIS method recognition rate for FERET face database

Fig. Comparison of recognition rate of HANFIS approach for different Mean Square Error and for different face databases

Conclusion From the experimental results it is clear that, our proposed approach HANFIS makes two primary advantages: It reduces the recognition time required Improves the recognition accuracy as the MSE reduces The main drawback in our method is, the Haralick feature extraction time is too large compared to others.

Future Work Since the feature extraction time is large, our future work involves, the usage of Genetic Algorithm for feature selection. This GA based HANFIS will produce far better results compared to other methods. It not only reduces the recognition time, but also the feature extraction time.