Integration of Colbp and Viola Jones Feature Extraction Methods in Gender Classification Based on Facial Image
Abstract
Nowadays face recognition still being a hot topics to be discussed especially it’s utility for gender classification. Gender classification is an easy task for human but it’s a challenging task for computers because it doesn’t have capability for recognizing human gender without feature extraction. There are still many researches about facial image feature extraction for gender classification. Geometry features and Texture Features are well perform features for gender classification. This paper will presents fusion of those feature in order to find an improvement for gender classifications task. Each features will be extracted using Viola Jones Algorithm and Compass Local Binary Pattern method. Both features will be combined using concatenated method in dataframe format. Viola Jones algorithm has an issues when detecting each facial regions so it causes outliers in each geometry features. The proposed method will be evaluated on color FERET dataset that contains facial images. Classification task will be done using Random Forest and Backpropagation. The result is fusion features present an improvement in gender classification using Backpropagation with 87% accuracy. It prove that proposed method perform better than single feature extraction method.
References
[2] U. D. Rosiani, R. A. Asmara, and N. Laeily, “PENERAPAN FACIAL LANDMARK POINT UNTUK KLASIFIKASI JENIS KELAMIN BERDASARKAN CITRA WAJAH,” JIP (Jurnal Informatika Polinema), 2019.
[3] A. Venugopal, Y. O. Yadukrishnan, and R. N. Nair, “A SVM based Gender Classification from Children Facial Images using Local Binary and Non-Binary Descriptors,” in Proceedings of the 4th International Conference on Computing Methodologies and Communication, ICCMC 2020, Institute of Electrical and Electronics Engineers Inc., Mar. 2020, pp. 631–634. doi: 10.1109/ICCMC48092.2020.ICCMC-000117.
[4] K. Prasada Rao, M. V. P. Chandra Sekhara Rao, and N. Hemanth Chowdary, “An integrated approach to emotion recognition and gender classification,” J Vis Commun Image Represent, vol. 60, pp. 339–345, Apr. 2019, doi: 10.1016/j.jvcir.2019.03.002.
[5] D. M. Abdulhussien and L. J. Saud, “AN EVALUATION STUDY OF FACE DETECTION BY VIOLA-JONES ALGORITHM,” World Economics & Finance Bulletin (WEFB), vol. 14, pp. 159–164, 2022, [Online]. Available: https://www.scholarexpress.net
[6] S. Fekri-Ershad, “Gender classification in human face images for smart phone applications based on local texture information and evaluated Kullback-Leibler divergence,” Traitement du Signal, vol. 36, no. 6, pp. 507–514, 2019, doi: 10.18280/ts.360605.
[7] M. Annalakshmi, S. M. M. Roomi, and A. S. Naveedh, “A hybrid technique for gender classification with SLBP and HOG features,” Cluster Comput, vol. 22, pp. 11–20, Jan. 2019, doi: 10.1007/s10586-017-1585-x.
[8] C. Zhang, H. Ding, Y. Shang, Z. Shao, and X. Fu, “Gender classification based on multiscale facial fusion feature,” Math Probl Eng, vol. 2018, 2018, doi: 10.1155/2018/1924151.
[9] B. Patel, R. P. Maheshwari, and B. Raman, “Compass local binary patterns for gender recognition of facial photographs and sketches,” Neurocomputing, vol. 218, pp. 203–215, Dec. 2016, doi: 10.1016/j.neucom.2016.08.055.
[10] M. Wahyu, B. Santoso, R. Cahya Wihandika, and M. A. Rahman, “Ekstraksi Ciri untuk Klasifikasi Jenis Kelamin berbasis Citra Wajah menggunakan Metode Compass Local Binary Patterns,” 2019. [Online]. Available: http://j-ptiik.ub.ac.id
[11] K. Chengeta and S. Viriri, A Survey on Facial Recognition based on Local Directional and Local Binary Patterns. Information Communications Technology and Society, 2018.
[12] A. Jaswante, A. U. Khan, and B. Gour, “Gender Classification Technique Based on Facial Features using Neural Network,” 2013. [Online]. Available: www.ijcsit.com
[13] A. Geetha, M. Sundaram, and B. Vijayakumari, “Gender classification from face images by mixing the classifier outcome of prime, distinct descriptors,” Soft comput, vol. 23, no. 8, pp. 2525–2535, Apr. 2019, doi: 10.1007/s00500-018-03679-5.
[14] M. N. Chaudhari, M. Deshmukh, G. Ramrakhiani, and R. Parvatikar, Face Detection using Viola Jones Algorithm and Neural Networks. Fourth International Conference on Computing Communication Control and Automation, 2018.
[15] E. L. Santoso, E. Setyati, and Y. Kristian, “Klasifikasi Citra Daun Memanfaatkan Angular Partition, Edge Detection dan Neural Network,” Insyst, vol. 1, no. 1, pp. 27–33, 2019, doi: 10.52985/insyst.v1i1.32.
[16] L. Breiman, “Random Forests,” Machine learning, vol. 45, no. 1, pp. 5-32, 2001.
[17] G. A. B. Suryanegara, Adiwijaya, and M. D. Purbolaksono, “Peningkatan Hasil Klasifikasi pada Algoritma Random Forest untuk Deteksi Pasien Penderita Diabetes Menggunakan Metode Normalisasi,” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 114–122, Feb. 2021, doi: 10.29207/resti.v5i1.2880.
[18] H. Tran, C. Dong, M. Naghedolfeizi, and X. Zeng, “Using cross-examples in viola-jones algorithm for thermal face detection,” in Proceedings of the 2021 ACMSE Conference - ACMSE 2021: The Annual ACM Southeast Conference, Association for Computing Machinery, Inc, Apr. 2021, pp. 219–223. doi: 10.1145/3409334.3452083.
[19] I. S. Hutomo and H. Wicaksono, “A smart door prototype with a face recognition capability,” IAES International Journal of Robotics and Automation (IJRA), vol. 11, no. 1, p. 1, Mar. 2022, doi: 10.11591/ijra.v11i1.pp1-9.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
