Facial Recognition using Locality Preserving Projection Algorithm


  • Adekunle Adeshina Department of Information Technology, University of Ilorin, Ilorin, Nigeria.
  • A.O. Basil High Performance Computing Research Laboratory, Nigeria


Facial Recognition Technology, Locality Preserving Projection, Criminal Investigation


Facial Recognition Technology (FRT) has become the research object of many in the recent times considering the challenges with enormous increase in human population. The face as the main axis in social relations plays an important role in the representation of human identity, which requires an increased level of security and the creation of exchange tricks for safe and recognizable evidence and innovations of individual authentication. Apparently, facial recognition is widely adopted for security reasons, and the uniqueness of human characteristics has increased the popularity of facial recognition technology systems worldwide. Law enforcement agencies faced the problem of the impossibility of proper investigation of criminal cases. Suspects are often difficult to catch and the wrong persons may be arrested due to the basis and methods adopted for the investigation. This study proposes a facial recognition system for the identification of criminals using Locality Preserving Projection (LPP) Algorithm. The development of the system involves specification of the functions resulting from the performance analysis obtained from other related systems, and the translation of the developed model into the design of the proposed system. The framework was evaluated by matching the faces of people extracted through special cameras with the images of people on a watch list. Watch lists that contain images of people, including people not suspected of wrong doings. Strategically, the implementation of the facial recognition system was achieved with Locality Preserving Projection (LPP) Algorithm to improve the feature extraction methods and dimensionality reduction techniques. Cascading Style Sheets (CSS) was used for describing the presentation of the document written in HTML language and JavaScript for the front-end for optimum web compatibility. A Python binding of the cross-platform Qt GUI toolkit and Python plugin were used to implement the graphical user interface for detecting and recognizing images and the presence of an individual upon entering the database library. Interestingly, the developed framework recorded accuracy and recognition >95% capacity under normal conditions, including lighting and distance from camera and at a reasonably cheaper cost in comparison with previously proposed Facial Recognition Technology


Adeshina, A.M., Hashim, R., Khalid, N.E.A., Abidin, S.Z.Z. (2012). Locating Abnormalities in Brain Blood Vessels using Parallel Computing Architecture. Interdiscip Sci Comput Life Sci 4,161–172.

Adeshina, A.M., Hashim, R., Khalid, N.E.A. (2014). CAHECA: Computer Aided Hepatocellular Carcinoma Therapy Planning. Interdiscip Sci Comput Life Sci 6, 222–234.

Adeshina, A.M., Hashim, R. (2015). ConnectViz: Accelerated Approach for Brain Structural Connectivity using Delaunay Triangulation. In: Interdisciplinary sciences: computational life sciences, pp 1–13.

Alrikabi, H.T.S., Aljazaery, I.A., Qateef, J.S., Alaidi, A.H.M., Al_airaji, R.M. (2022). Face Patterns Analysis and Recognition System based on Quantum Neural Network. International Journal of Interactive Mobile Technologies 16(8).

Ge, H., Zhu, Z., Dai, Y., Wang, B., Wu, X. (2022). Facial Expression Recognition based on Deep Learning. Computer Methods and Programs in Biomedicine. Vol. (215). March.

Hammouche, R., Attia, A., Akhrouf, S., Akhtar, Z. (2022). Gobor Filter Bank with Deep Autoencoder Based Face Recognition System. Expert Systems with Applications, Vol. (197), I July, 2022.

He, X. & Niyogi, P. (2003). Locality Preserving Projections, In Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press.

Jin, B., Cruz, L., Goncalves, N. (2022). Pseudo RGD-D Face Recognition. IEEE Sensors Journal 22(22). 21780-21794.

Liyakat, K.K.S., Mane, V.A, Paradeshi, K.P., Kadam D.B., Pandyaji, K.K. (2022). Journal of Algebraic Statisctics. 13(3), 3676 - 3684.

Rana, S., Deepu, MP.,Sivanantham, S., Sivasankaran, K. (2016). The Face detection system using FPGA. In: IC-GET 2015. Proceedings of 2015 Online International Conference on Green Engineering and Technologies Google Scholar.

Shermina, J. (2010). Application of Locality Preserving Projections in Face Recognition. International Journal of Advanced Computer Science and Applications. 1(3)

Sun, Y., Ren, Z., Zheng, W. (2022). Research on Face Recognition Algorithm based on Image Processing. Computational Intelligence and Neuroscience.

Yu, Z., Dong, Y., Cheng, J., Sun, M., Su, Feng, S. (2022). Research on Face Recognition Classification Based on Improved GoogleNet. Hindawi. Security and Communication Networks. 2022.