International Journal of Image Processing and Pattern Recognition

Latest articles

• Published Subscription 26 Jun 2026

  AI-Based Deepfake Voice Detection Model for Hindi Language

  By Ashutosh Radheshyam Gaur, Atharva Santosh Dhumal, Ashwini Renavikar

  Abstract: Most current research on deepfake voice detection focuses heavily on English or Mandarin, creating a significant security gap for Hindi speakers. To address this, we developed a detection framework specifically tailored for the Hindi language using a lightweight approach: Mel-Frequency Cepstral Coefficient (MFCC) feature extraction paired with an XGBoost classifier. We trained the system on a large dataset comprising over 99,000 real voice samples and roughly 3,800 synthetic samples, each …

  Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 2, 2026 Read article →

• Published Subscription 1 May 2026

  DEVELOPING A DRIVER DROWSINESS DETECTION SYSTEM (DDDS)

  By Pranjal, Navin Kumar Tyagi, R. Prasad

  Abstract: Driver drowsiness accounts for over 18% of all traffic accidents in India, making it a major contributing factor. This study proposes a real-time, edge-based Driver Drowsiness Detection System (DDDS) that detects early fatigue signs and alerts drivers prior to accidents. The system employs a multimodal, bimodule strategy. In order to track face traits such eye closure, blink rate, yawning, head position, and gaze direction, the first module uses computer vision. …

  Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 2, 2026 Read article →

• Published Subscription 18 Feb 2026

  A Study on CNN-Based Image Mosaicing

  By Kazi Kutubuddin Sayyad Liyakat, Heena T Shaikh

  Abstract: The intricate tapestry of image mosaicing, while a cornerstone of computer vision, has long been constrained by the rigidity of classical, geometry-driven pipelines. Hand-crafted feature detectors and homography estimators, while effective in controlled environments, prove brittle when confronted with the challenges of real-world scenes: significant parallax, dynamic moving objects, repetitive textures, and illumination variance. This work introduces a fundamental paradigm shift, proposing a deep convolutional neural network (CNN) architecture designed …

  Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 1–7 Read article →

• Published Subscription 18 Feb 2026

  Advanced Classification of Diabetic Retinopathy Using GAN and Deep Residual CNN Model

  By Rama Devi C, Rithanya K.R, Shakti P, Subhashini G

  Abstract: Diabetic retinopathy (DR) is a leading cause of blindness worldwide, impacting the largest number of individuals who have diabetes. Early identification of diabetic retinopathy may help to prevent severe effects; however, the very limited availability of labeled datasets, and the possibility of overfitting of the model, presents obstacles to obtaining an accurate diagnosis. In this research, a hybrid deep learning strategy is introduced, where Generative Adversarial Networks (GANs) are used …

  Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 8–16 Read article →

• Published Subscription 18 Feb 2026

  Vani-Adapt: A Zero-Shot Accent Trans-Adaptation Framework for Robust Indic Speech Recognition

  By Jyotirmoyee Mandal, Kunal Halder, Kakali Das

  Abstract: In countries like India with multilingual and accent-rich dialects, speech-based human–computer interaction is important to expand digital services for better accessibility. Even with recent advancements in Automatic Speech Recognition (ASR), existing systems are still very reactive to regional accents and non-standard speech patterns which is not suitable for seamless experience. Traditional perspectives rely on accent-specific fine-tuning, which is unrealistic for real-world deployment and requires a lot of labeled data, which …

  Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 17–22 Read article →

• Published Subscription 18 Feb 2026

  Enhancing Facial Expression Analysis Using CNNs: Insights from ML and Deep Learning Comparisons

  By Aashi, Shobhit Jaiswal, Mandeep

  Abstract: Emotion recognition from facial manifestations increases the interaction between humans and computers by enabling the interaction between humans and computers to effectively explain human feelings. This study presents a comparative analysis of three approaches to the emotional recognition of faces: a custom Convolutional Neural Network (CNN) model, Traditional Machine Learning (ML) methods, and other deep learning architectures such as VGG, ResNet, and Hybrid CNN–RNN models. The proposed CNN-based system, developed …

  Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 23–31 Read article →

• Published Subscription 18 Feb 2026

  A Comprehensive Review of Convolutional Neural Network Architectures and Evolution

  By Swarnali Kundu, Biswasri Datta, Tousif Parvej, Pritam Pal, Fakruddin Ali Ahmed

  Abstract: Convolutional Neural Networks (CNNs) have become a foundational deep learning framework in computer vision because they can automatically extract layered, increasingly complex features directly from raw image data. CNNs use convolutional, pooling, and activation layers to extract spatial patterns from basic edges to intricate object components, drawing inspiration from the human visual cortex. An overview of CNNs’ basic architecture is given in this study, along with an explanation of important …

  Published in International Journal of Image Processing and Pattern Recognition · Vol. 12, Issue 1, 2026 · pp. 32–40 Read article →

• Published Ahead of print Subscription 26 Dec 2025

  Gourmet Sage Using Deep Learning With Calories Chart

  Abstract: Gourmet Sage via Deep Learning with Calories Chart is an innovative system that merges advanced deep learning technology with nutrition science to help users make healthier dietary choices. By leveraging YOLOv8, a cutting-edge object detection model, the system allows real-time identification of food items, estimating their calorie content and ingredient composition with remarkable accuracy. It goes beyond detection by providing a comprehensive nutritional breakdown, including macronutrients (proteins, fats, carbs) and …

  Published in International Journal of Image Processing and Pattern Recognition Read article →

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