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All Journal International Journal of Reconfigurable and Embedded Systems (IJRES)
Rama Devi, K.
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Economics, Econometrics & Finance

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Journal : International Journal of Reconfigurable and Embedded Systems (IJRES)

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Real-time face detection and local binary patterns histograms-based face recognition on Raspberry Pi with OpenCV Chandrasekaran, Bharanidharan; Karunkuzhali, D.; Kandasamy, V.; DIllibabu, M.; Rama Devi, K.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 14, No 2: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v14.i2.pp527-537

Abstract

This paper presents a practical end-to-end paper demonstrating real-time face recognition using a Raspberry Pi and open source computer vision library (OpenCV) consisting of three main stages: training the recognizer, real-time recognition, and face detection and data gathering. The paper offers a comprehensive guide for enthusiasts venturing into computer vision and facial recognition. Employing the Haar Cascade classifier for accurate face detection and the local binary patterns histograms (LBPH) face recognizer for robust training and recognition, the paper ensures a thorough understanding of key concepts. The step-by-step process covers software installation, camera testing, face detection, data collection, training, and real time recognition. With a focus on the Raspberry Pi platform, this paper serves as an accessible entry point for exploring facial recognition technology. Readers will gain insights into practical implementation, making it an ideal resource for learners and hobbyists interested in delving into the exciting realm of computer vision.
Optimizing call center agent efficiency through deep learning-based classifications using SMFCCAE Periyasamy, Ramachandran; Govindaraji, Manikandan; Nasurulla, I.; Srinivasan, V.; Rama Devi, K.
International Journal of Reconfigurable and Embedded Systems (IJRES) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijres.v15.i1.pp31-41

Abstract

Call centers are vital to business operations worldwide, acting as the primary interface between companies and their customers. They handle customer inquiries, manage complaints, and facilitate telephonic sales, making them essential to customer service. However, ensuring quality in the call center industry remains challenging, primarily due to the heavy reliance on call center representatives (CSRs) who manage high volumes of calls. Traditional methods of evaluating CSR performance often rely on manual assessments of small call samples, which can be time-consuming and limited in scope. With the advancement of deep learning techniques (DLTs), there is an opportunity to more accurately assess CSR performance. This study introduces the selecting minimal features for call center agents efficiency (SMFCCE) approach, which optimizes feature selection from CSR data to enhance classification accuracy and speed. The proposed method achieves approximately 85% accuracy, offering valuable insights and recommendations for improving overall call center operations.
Co-Authors Chandrasekaran, Bharanidharan DIllibabu, M. Govindaraji, Manikandan Kandasamy, V. Karunkuzhali, D. Nasurulla, I. Periyasamy, Ramachandran Srinivasan, V.