Face Recognition Application Based on Convolutional Neural Network for Searching Someone’s Photo on External Storage

I Putu Arya Dharmaadi, Deden Witarsyah, I Putu Agung Bayupati, Gusti Made Arya Sasmita


Digital photos are often defined as personal archives collected long ago and are stored on a large enough storage media such as an external hard disk or flash disk. Problems arise when someone wants to find photos of themselves or others in tons of photo collections. Searching manually, such as opening a photo file or folder one by one, will certainly be very troublesome. Based on these problems, this study designed an application for searching certain photos based on the similarity of the inserted face photo. This application is built for computer or laptop devices, which was developed by using the Python programming language and Dlib module that applied the face recognition method through the combination of Convolutional Neural Network (CNN), FaceNet Embedding, and Triplet Loss for matching faces. The recognition scheme starts from face detection, face alignment, face encoding, and face classification stage. Our application is very handy to run in looking for particular face images on external storage compared to prior studies. We have done experimental research, demonstrating that the application can find almost all image files the user is looking for. In addition to the result in the form of an application, this study contributes to exploring the performance of the Dlib module, in terms of precision and recall rate, which could not recognize non-frontal face images well. We encourage other researchers to address this limitation in further studies.


Photo searching; convolutional neural network; face recognition; python; dlib.

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Z. Wei et al., “AutoPrivacy: Automatic privacy protection and tagging suggestion for mobile social photo,” Comput. Secur., vol. 76, pp. 341–353, 2018, doi: https://doi.org/10.1016/j.cose.2017.12.002.

Y. Lei, Y. Chen, L. Iida, B. Chen, H. Su, and W. H. Hsu, “Photo Search by Face Positions and Facial Attributes on Touch Devices,” in Proceedings of the 19th ACM international conference on Multimedia, 2011, pp. 651–654.

D. Wang, C. Otto, and A. K. Jain, “Face Search at Scale,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 39, no. 6, pp. 1122–1136, 2017, doi: 10.1109/TPAMI.2016.2582166.

H. Kang and B. Shneideman, “Visualization Methods for Personal Photo Collections : Browsing and Searching in the PhotoFinder,” in IEEE International Conference on Multimedia and Expo (ICME), 2000, vol. 03, pp. 1539–1542.

M. Wang and W. Deng, “Deep face recognition: A survey,” Neurocomputing, vol. 429, pp. 215–244, 2021, doi: https://doi.org/10.1016/j.neucom.2020.10.081.

G. Guo and N. Zhang, “A survey on deep learning based face recognition,” Comput. Vis. Image Underst., vol. 189, p. 102805, 2019, doi: 10.1016/j.cviu.2019.102805.

I. Masi, Y. Wu, T. Hassner, and P. Natarajan, “Deep Face Recognition: A Survey,” Proc. - 31st Conf. Graph. Patterns Images, SIBGRAPI 2018, pp. 471–478, 2018, doi: 10.1109/SIBGRAPI.2018.00067.

R. Jafri and H. R. Arabnia, “A survey of face recognition techniques,” J. Inf. Process. Syst., vol. 5, no. 2, pp. 41–68, 2009.

M. Wang and W. Deng, “Deep Face Recognition: A Survey,” CoRR, vol. abs/1804.0, 2018, doi: 10.1109/SIBGRAPI.2018.00067.

M. Lal, K. Kumar, R. H. Arain, A. Maitlo, S. A. Ruk, and H. Shaikh, “Study of Face Recognition Techniques: A survey,” Int. J. Adv. Comput. Sci. Appl., vol. 9, no. 6, pp. 42–49, 2018, doi: 10.14569/IJACSA.2018.090606.

Q. Wang and G. Guo, “Benchmarking deep learning techniques for face recognition,” J. Vis. Commun. Image Represent., vol. 65, p. 102663, 2019, doi: 10.1016/j.jvcir.2019.102663.

Y. Taigman, M. A. Ranzato, T. Aviv, and M. Park, “DeepFace : Closing the Gap to Human-Level Performance in Face Verification,” 2014.

S. Z. Li and A. K. Jail, Handbook of Face Recognition. London: Springer, 2011.

M. P. Beham and S. M. M. Roomi, “A review of face recognition methods,” Int. J. Pattern Recognit. Artif. Intell., vol. 27, no. 4, pp. 13560051–135600535, 2013, doi: 10.1142/S0218001413560053.

S. Haykin, Neural Networks and Learning Machines Third Edition, vol. 3. New Jersey: Pearson Education, 2009.

A. Elmahmudi and H. Ugail, “Deep face recognition using imperfect facial data,” Futur. Gener. Comput. Syst., vol. 99, pp. 213–225, 2019, doi: 10.1016/j.future.2019.04.025.

H. Habibi, A. Elnaz, J. Heravi, P. Application, and T. Detection, Guide to Convolutional Neural Networks. Springer, 2017.

Y. Cai, Y. Lei, M. Yang, Z. You, and S. Shan, “A fast and robust 3D face recognition approach based on deeply learned face representation,” Neurocomputing, vol. 363, pp. 375–397, 2019, doi: 10.1016/j.neucom.2019.07.047.

N. Dalal and B. Triggs, “Histograms of Oriented Gradients for Human Detection,” 2005.

L. Boussaad and A. Boucetta, “Deep-learning based descriptors in application to aging problem in face recognition,” J. King Saud Univ. Comput. Inf. Sci., 2020, doi: 10.1016/j.jksuci.2020.10.002.

A. Geitgey, “Machine Learning is Fun! Part 4: Modern Face Recognition with Deep Learning,” Medium.com, 2016.

D. S. Trigueros, L. Meng, and M. Hartnett, “Face Recognition : From Traditional to Deep Learning Methods,” arXiv e-prints, p. arXiv:1811.00116, 2018.

F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet : A Unified Embedding for Face Recognition and Clustering,” 2015.

L. Shi, X. Wang, and Y. Shen, “Research on 3D face recognition method based on LBP and SVM,” Optik (Stuttg)., vol. 220, p. 165157, 2020, doi: https://doi.org/10.1016/j.ijleo.2020.165157.

D. E. King, “Dlib-ml : A Machine Learning Toolkit,” J. Mach. Learn. Res., vol. 10, pp. 1755–1758, 2009.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” CoRR, vol. abs/1512.0, 2015.

D. E. King, “dlib-models,” Github, 2018.

M. Junker, R. Hoch, and A. Dengel, “On the Evaluation of Document Analysis Components by Recall, Precision, and Accuracy,” Proc. Fifth Int. Conf. Doc. Anal. Recognition. ICDAR’99, pp. 713–716, 1999.

M. Taskiran, N. Kahraman, and C. E. Erdem, “Face recognition: Past, present and future (a review),” Digit. Signal Process. A Rev. J., vol. 106, p. 102809, 2020, doi: 10.1016/j.dsp.2020.102809.

L. Zhou, W. Li, Y. Du, B. Lei, and S. Liang, “Adaptive illumination-invariant face recognition via local nonlinear multi-layer contrast feature,” J. Vis. Commun. Image Represent., vol. 64, p. 102641, 2019, doi: 10.1016/j.jvcir.2019.102641.

DOI: http://dx.doi.org/10.18517/ijaseit.12.3.11666


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