Journal of Image Processing & Pattern Recognition Progress

Latest articles

• Published Subscription Original Research 26 Jun 2026

  AI-Based Outfit Rating and Suggestion System

  By Sneha Vivek Pandey, Pranali Parate, Shweta Waghmare

  Abstract: The increasing demand for personalised fashion advice in the digital era has highlighted the need for intelligent, automated styling solutions. The AI-Based Outfit Rating and Suggestion System is a web- based platform that assists users in evaluating and improving their clothing choices through intelligent image analysis. Unlike conventional fashion applications that merely identify garment categories or suggest purchases, this system performs a holistic assessment of complete outfits by analysing colour …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 2, 2026 Read article →

• Published Subscription Review Article 16 Jun 2026

  Real-Time Attendance System using Face Recognition Using OpenCV and Firebase Realtime Database

  By Poonam Chakravarty, Jigar Pandya

  Abstract: The Facial Recognition Attendance System now a days revolutionizes traditional attendance tracking by seamlessly integrating cutting-edge image processing with the capabilities of Firebase Realtime Database. This user-friendly solution simplifies and transforms the attendance management experience. Imagine an intuitive interface utilizing facial recognition technology to effortlessly track attendance. Leveraging advanced face detection algorithms and the enchantment of computer vision, our system ensures accurate face recognition, making each individual unmistakably identifiable. Beyond …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 2, 2026 Read article →

• Published Subscription Original Research 28 Apr 2026

  Optimizing Mango Harvest Timing in the Nasik Region (Maharashtra, India) by CNNs (Residual Network 101)

  By Rajesh Kapur, Rajesh Kulkarni

  Abstract: The determination of optimal harvest timing is one of the most critical decisions in mango production, directly affecting postharvest quality, market value, transportation resilience, and export readiness. In regions such as Nashik, Maharashtra—one of India’s major fruit- producing belts—the climatic variability, cultivar differences, monsoon patterns, and market- driven pressures make accurate harvest timing essential. Traditional maturity assessment relies on subjective visual inspection, specific gravity, or destructive testing, each of which …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 Read article →

• Published Subscription Review Article 26 Feb 2026

  SkinSight: Design and Implementation of an Intelligent Skin Type Detection System

  By Diya Goswami, Sayantika Laskar

  Abstract: Identifying an individual’s skin type accurately is essential for creating personalized dermatological treatments and formulating skincare products that genuinely meet user needs. In this project, a real- time skin type classification system is developed using a combination of convolutional neural networks (CNNs) and modern computer vision techniques. The system processes live video streams, isolates the facial region through Haar cascade–based detection, and applies a series of preprocessing steps to enhance …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 35–45 Read article →

• Published Subscription Original Research 26 Feb 2026

  Face Recognition Attendance System Using Local Binary Pattern Histogram Algorithm

  By Yash Rawat, Harsh Tongar, Hemant

  Abstract: Maintaining accurate and tamper-proof attendance records in educational and corporate environments has long been a challenge due to the limitations of manual and biometric systems. This study introduces the development and deployment of a contactless, automated attendance system that utilizes facial recognition through the local binary pattern histogram (LBPH) algorithm. The primary goal is to offer a secure and efficient substitute for conventional attendance methods by harnessing the power of …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 29–34 Read article →

• Published Subscription Review Article 25 Feb 2026

  Automated Car License Plate Detection and Recognition Using Deep Learning

  By Prachi Pravin Durge, Suraj V. Dhole

  Abstract: The use of automated license plate detection and recognition (ALPR) systems to automate processes such as number plate detection is gaining popularity in traffic control, security, and law enforcement. This research focuses on achieving more accurate and efficient detection and recognition of number plates by leveraging deep learning techniques. The systems outlined in this study aim to improve the effectiveness of ALPR systems using advanced convolutional neural networks (CNNs) and …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 23–29 Read article →

• Published Subscription Original Research 25 Feb 2026

  Animal Species Prediction Using Deep Learning

  By Sehajpreet Kaur, Arshveer Kaur, Jaspreet Kaur

  Abstract: In the face of escalating biodiversity loss, effective monitoring of animal species is critical for conservation efforts. This study presents a deep learning approach for species detection and a multimodal feature identification technique for animals vulnerable to poaching. The suggested prediction system recognizes objects automatically by the application of deep learning techniques to detect objects and then recognize them by using computer vision techniques, and it is triggered when an …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 14–22 Read article →

• Published Subscription Review Article 7 Nov 2025

  A Review of Automated Pomegranate Disease Detection and Classification Using Machine Learning

  By Vidya Shejwal, K.J. Karande, Anjali C. Pise

  Abstract: The abstract outlines a research study focused on developing an automated system for detecting and classifying diseases that affect pomegranate fruits. Pomegranates, like many other crops, are vulnerable to several types of diseases that appear as visible colored spots on the fruit’s surface. These visible symptoms, such as lesions or discoloration, can significantly impact the fruit’s quality, market value, and yield. Therefore, timely and accurate identification of such diseases is …

  Published in Journal of Image Processing & Pattern Recognition Progress · Vol. 13, Issue 1, 2026 · pp. 01–13 Read article →

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