Detection of duplicate and non-face images in the eRecruitment applications using machine learning techniques

Manjunath K. E., Yogeen S. Honnavar, Rakesh Pritmani, Sethuraman K.


The objective of this work is to develop methodologies to detect, and report the noncompliant images with respect to indian space research organisation (ISRO) recruitment requirements. The recruitment software hosted at U. R. rao satellite centre (URSC) is responsible for handling recruitment activities of ISRO. Large number of online applications are received for each post advertised. In many cases, it is observed that the candidates are uploading either wrong or non-compliant images of the required documents. By non-compliant images, we mean images which do not have faces or there is not enough clarity in the faces present in the images uploaded. In this work, we attempt to address two specific problems namely: 1) To recognise image uploaded to recruitment portal contains a human face or not. This is addressed using a face detection algorithm. 2) To check whether images uploaded by two or more applications are same or not. This is achieved by using machine learning (ML) algorithms to generate similarity score between two images, and then identify the duplicate images. Screening of valid applications becomes very challenging as the verification of such images using a manual process is very time consuming and requires large human efforts. Hence, we propose novel ML techniques to determine duplicate and non-face images in the applications received by the recruitment portal.


Face detection, Haar cascade classifier, Histogram, Opencv, Template matching

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Pratap K.M, Yogeen S. Honnavar, Rakesh kumar, Sunil Kumar D, and Gurumoorthy D, “e-Recruitment in ISRO: Advantages andChallenges,” in Workshop on Computer and Information Technology (WCIT), 2008.

Y. Honnavar, K. M. Pratap, R. Kumar, S. Ramanathan, and G. N. V. Prasad, “Enabling Digital Payments - a case study,” in ISRO Symposium on Computers and Information Technology (ISCIT), 2018.

Lewis, M. Bevan, and H. D. Ellis, “How we detect a face: A survey of psychological evidence,” International Journal of Imaging Systems and Technology, vol. 13, no. 1, pp. 3-7, September 2003.

R. Jafri and H. Arabnia, “A Survey of Face Recognition Techniques,” Journal of Information Processing Systems, vol. 5, no. 2, pp. 41-68, 2009. doi: 10.3745/JIPS.2009.5.2.041

N. Degtyarev and O. Seredin, “Comparative Testing of Face Detection Algorithms,” Image and Signal Processing, (Springer), vol. 6134, 2010. doi: 10.1007/978-3-642-13681-8 24

C. Zhang and Z. Zhang, “A Survey of Recent Advances in Face Detection,” Technical Report - Microsoft Research Publication, MSR-TR-2010-66, 2010.

M. Roomi and M. P. Beham, “A Review of Face Recognition Methods,” Circuits, Systems, and Signal Processing, (Springer), Vol. 27, No. 4, pp 1-35, 2013. doi: 10.1142/S0218001413560053

S. S. Farfade, M. Saberian, and L. Li, “Multi-view Face Detection Using Deep Convolutional Neural Networks,” International Conference on Multimedia Retrieval (ICMR), 2015.

Q. Hua, C. Dong, and F. Zhang, “A Novel Approach to Face Verification Based on Second-Order FacePair Representation,” Hindawi Complexity (Wiley), pp. 1-10, 2018. doi: /10.1155/2018/2861695

Y. Kortli, M. Jridi, A. A. Falou, and M. Atri, “Face Recognition Systems: A Survey,” Sensors, MDPI, vol. 20, pp. 3-34, 2020. doi: 10.3390/s20020342

Robert Frischholz. Face Detection & Recognition Homepage. [Online]. Available: [Accessed Oct. 07, 2020].

A. K. Upadhyay and K. Khandelwal , “Applying artificial intelligence: implications for recruitment,” Strategic HR Review, 2018. doi: 10.1108/SHR-07-2018-0051

E. T. Albert, “AI in talent acquisition: a review of AI-applications used in recruitment and selection,” Strategic HR Review, vol. 18(5), pp. 215-221, 2019. doi: 10.1108/SHR-04-2019-0024

S. Weinert, E. Gnther, E. Rueger-Muck, G. Raab, “Artificial intelligence in personnel selection and its influence on employer attractiveness,” Cross-Cultural Business Conference, 2020. doi: 10.1007/s00034-017-0568-8

N. Nawaz, “Artificial Intelligence Face Recognition for applicant tracking system,” International Journal of Emerging Trends in Engineering Research, vol. 7, no. 12, pp. 895-901, 2019. doi:


N. Nawaz, “ Artificial Intelligence Is Transforming Recruitment Effectiveness in CMMI Level Companies,” International Journal of Advanced Trends in Computer Science and Engineering, vol. 8(6), 2019.

P. v. Esch, J. S. Black, and J. Ferolie, “Marketing AI recruitment: The next phase in job application and selection,” Computers in Human Behavior, vol. 90, pp. 215-222, 2019. doi: 10.1016/j.chb.2018.09.0098

P. Viola and M. Jones, “Rapid Object Detection using a Boosted Cascade of Simple Features,” in IIEEE Conference on Computer Vision and Pattern Recognition, vol. 1, 2001, pp. 115–118.

Opencv Python Tutorials. Face Detection using Haar Cascades. [Online]. Available: tutorials/py objdetect

/py face detection/py face detection.html

Eduard Tyantov. Face Recognition : From Scratch To Hatch. [Online]. Available:

M. J. Swain and D. H. Ballard, “Color indexing,” International Journal of Computer Vision, vol. 7, pp. 11-32, 1991.

Massimiliano Patacchiola. (2016) The Simplest Classifier: Histogram Comparison. [Online]. Available:

R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice. Wiley, ISBN 978-0-470-51706-2, 2009.

C. T. Yuen, M. Rizon, W. S. San, and T. C. Seong, “Facial Features for Template Matching Based Face Recognition,” American Journal of Engineering and Applied Sciences, vol. 3(1), pp. 899-903, 2010.



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