Face Detection with Structural Coordinates for the Estimation of Patterns Using Machine Learning Model

Ya Liu; Jian Zhang

doi:10.69996/jcai.2023004

Authors

Ya Liu Department of Electrical Automation,Hebei University of Water Resources and Electric Engineering,Cangzhou, 061001,China Author
Jian Zhang Department of Electrical Automation,Hebei University of Water Resources and Electric Engineering,Cangzhou, 061001,China. Author

DOI:

https://doi.org/10.69996/jcai.2023004

Keywords:

Structural coordinates analysis, machine learning model, computer vision, biometrics, face detection

Abstract

This research paper investigates an innovative approach to face detection by combining structural coordinates analysis with machine learning techniques. The study introduces a method wherein facial landmarks’ structural coordinates are utilized as crucial input features for training a machinelearning model. Integrating these coordinates enhances the model’s ability to discern intricate facial features, thereby improving the precision of face detection. The paper analyzes the model’s performance, evaluating key metrics such as accuracy, precision, recall, and F1 Score. The results demonstrate notable success, with accuracy exceeding 90%, affirming the effectiveness of the proposed methodology. Additionally, the paper provides insights from a confusion matrix, offering a nuanced understanding of the model’s ability to classify positive and negative instances correctly. Sample image predictions further illustrate the practical implications of the proposed approach, showcasing instances of both accurate and challenging detections. This research contributes to the advancement of face detection technology and opens avenues for broader applications, ranging from biometric security systems to interactive technologies reliant on facial recognition. The synergistic integration of structural coordinates and machine learning in face detection signifies a promising avenue for future computer vision and biometrics developments.

References

[1] S. K.Srivastava, S.Katiyar and S. Kumar, “Pattern matching using face recognition system,” In Soft Computing: Theories and Applications: Proceedings of SoCTA 2020, vol.1, pp. 161-171, 2022.

[2] S.Dereli and A.R. Ünsal, “A new microarchitecture hardware proposal for the use of the improved template matching method in face similarity detection,” Microprocessors and Microsystems, vol.101, pp.104908, 2023.

[3] L.Wiskott, J.M.Fellous, N.Krüger and C.Von Der Malsburg, “Face recognition by elastic bunch graph matching,” In Intelligent biometric techniques in fingerprint and face recognition, pp. 355-396, 2022.

[4] N. M. I.Uddin, A. J.Moshayedi and Y. Shuxin, “The face detection/recognition, perspective and obstacles in robotic: a review,” EAI Endorsed Transactions on AI and Robotics, vol.1, no.1, pp.e14-e14, 2022.

[5] N. T.Abdulsada and S. M. Ali, “ Human face detection in a crowd image based on template matching technique,” In AIP Conference Proceedings, vol. 2437, no. 1, 2022.

[6] S.Hangaragi, T.Singh and N. Neelima, “Face detection and recognition using face mesh and deep neural network,” Procedia Computer Science, vol.218, pp.741-749, 2023.

[7] D.Zeitlyn, E.Coto and A. Zisserman, “Bounding an archiving: assessing the relative completeness of the Jacques Toussele archive using pattern-matching and face-recognition,” Visual Studies, vol.38,no.3-4, pp.523-547, 2023.

[8] M. I. H.Chowdhury, N. M.Sakib, S. M.Masum Ahmed, M.Zeyad, M. A. A.Walid et al., “Human face detection and recognition protection system based on machine learning algorithms with proposed ar technology,” Advances in Augmented Reality and Virtual Reality, pp.177-192, 2022.

[9] S.Sharma, “Template matching approach for face recognition system,” International journal of signal processing systems, vol.1, no.2, pp.284-289, 2013.

[10] S. K.Srivastava, S.Katiyar and S. Kumar, “Pattern matching using face recognition system,” In Soft Computing: Theories and Applications: Proceedings of SoCTA 2020, vol.1, pp. 161-171, 2022.

[11] R.Hammouche, A.Attia, S.Akhrouf and Z. Akhtar, “Gabor filter bank with deep autoencoder based face recognition system,” Expert Systems with Applications, vol.197, pp.116743, 2022.

[12] L.Wang and Q. Liu, “Discriminant distance template matching for image recognition,” Machine Vision and Applications, vol.33, no.6, pp.91, 2022.

[13] A. A.Khan, A. A.Shaikh, Z. A.Shaikh, A. A.Laghari and S. Karim, “IPM-Model: AI and metaheuristic-enabled face recognition using image partial matching for multimedia forensics investigation with genetic algorithm,” Multimedia Tools and Applications, vol.81, no.17, pp.23533-23549, 2022.

[14] H. T. S.ALRikabi, I. A.Aljazaery, J. S.Qateef, A. H. M.Alaidi and M. Roa’a, “ Face patterns analysis and recognition system based on Quantum Neural Network QNN,” iJIM, vol.16, no.08, pp.35, 2022.

[15] K.Raju, B.Chinna Rao, K.Saikumar and N. Lakshman Pratap, “An optimal hybrid solution to local and global facial recognition through machine learning,” A fusion of artificial intelligence and internet of things for emerging cyber systems, pp.203-226, 2022.

[16] M. I. H.Chowdhury, N. M.Sakib, S. M.Masum Ahmed, M.Zeyad, M. A. A.Walid et al., & “Human face detection and recognition protection system based on machine learning algorithms with proposed ar technology,” Advances in Augmented Reality and Virtual Reality, pp.177-192, 2022.

[17] V.Salari, D.Paneru, E.Saglamyurek, M.Ghadimi, M. Abdar et al., “Quantum face recognition protocol with ghost imaging,” Scientific Reports, vol.13, no.1, pp.2401, 2023.

[18] W.Yan, M.Shabaz and M. Rakhra, “Research on nonlinear distorted image recognition based on artificial neural network algorithm,” Journal of Interconnection Networks, vol.22, no.Supp06, pp.2148002, 2022.

[19] M.Tamilselvi and S.Karthikeyan, “An ingenious face recognition system based on HRPSM_CNN under unrestrained environmental condition,” Alexandria Engineering Journal, vol.61, no.6, pp.4307-4321, 2022.

[20] H.Yang and Z. Liu, “Image recognition technology of crop diseases based on neural network model fusion,” J. Electron. Imaging, vol.32, no.1, pp.11202, 2023.

[21] T.Kamyab, A.Delrish, H.Daealhaq, A.M.Ghahfarokhi and F. Beheshtinejad, “Comparison and review of face recognition methods based on gabor and boosting algorithms,” International Journal of Robotics & Control Systems, vol.2, no.4, 2022.

[22] T.Cheeseman, K.Southerland, J.Park, M.Olio, K.Flynn et al., “Advanced image recognition: a fully automated, high-accuracy photo-identification matching system for humpback whales,” Mammalian Biology, vol.102, no.3, pp.915-929, 2022.