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7 articles for “Score Fusion Technique”
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Multi-Modal Iris and Retina Recognition System using Discrete Hidden Markov Model based Score Fusion Technique
Abstract: The contribution of this work is to propose a model of multi-modal iris and retina recognition system where Hidden Markov Model based score fusion technique has been used for decision fusion approach. Two different uni-modal techniques such as iris recognition and retina recognition outputs are combined by using baseline reliability ratio based score fusion technique. CASIA iris dataset and DRIVE retinal dataset have been used to acquire the iris and …
Published in Current Trends in Information Technology · Vol. 5, Issue 2, 2015 · pp. 7–12 Read article
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Face and Iris based Multimodal Person Identification using Likelihood Ratio Score Fusion Approach.
Abstract: AbstractThe aim of this paper is to propose a multimodal biometric system where likelihood ratio score fusion technique has been used to combine face and iris uni-modal biometric system outputs. Active Shape Model (ASM) has been used to extract the facial features and standard methods have been applied to effectively extract the human iris features. To evaluate the output of each individual modality of face and iris, Discrete Hidden Markov …
Published in Journal of Computer Technology & Applications · Vol. 6, Issue 3, 2015 · pp. 60–67 Read article
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Facial Recognition System Based on Score-level Fusion of LBP and Gabor Feature Extraction Techniques
Abstract: Face recognition is gaining recognition over the last few decades. Nowadays, this technology is highly used in smartphones to unlock the phone by sensing the face of an individual. In our paper, we aim to maximize recognition rates by performing the score-level fusion of two feature extraction techniques: Local Binary Pattern (LBP) and Gabor features. These two techniques on fusion provide better recognition rates as compared to the recognition rates …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 7, Issue 1, 2020 · pp. 10–14 Read article
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An Approach of Likelihood Ratio Score Fusion for Appearance and Shape based Face Recognition
Abstract: AbstractThis paper deals with an approach of likelihood ratio based score fusion technique where appearance and shape based facial features have been used to enhance the efficiency of existing face recognition system. Active Shape Model (ASM) has been used to extract the appearance and shape based facial features. Two different types of features have been used in this work in such a way that when the appearance based feature contains …
Published in Journal of Computer Technology & Applications · Vol. 4, Issue 3, 2013 · pp. 1–8 Read article
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Recipe-Fusion: Multimodal Food Recipe Recommendation System
Abstract: The food recipe recommendation system using data science is a software solution designed to help users discover new and delicious food options based on their food history and other relevant data. This system recommends various recipes based on the input given by the user and it helps to filter out the recipes on course type, diet type, and nature of the food (including non-veg, and veg) using a recommendation technique. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 11, Issue 3, 2024 · pp. 82–91 Read article
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Diffusion-Based Enhancement of Low-SNR Time- Frequency Signals
Abstract: Traditional enhancing techniques are useless in low signal-to-noise ratio (LSNR) situations because noise drastically interferes with communication signals. Based on an enhanced DiffBIR model, this paper suggests a dual-stage signal improvement approach that combines diffusion with deep learning. By combining the Inception module for multi-scale feature extraction with the Pixel Fusion Attention (PFA) module for significant region highlighting, the model improves signal recovery in the time- frequency domain. Experiments show …
Published in Current Trends in Signal Processing · Vol. 16, Issue 2, 2026 · pp. 15–27 Read article
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EMOTION RECOGNITION FROM ELECTROENCEPHALOGRAM SIGNAL AND EYE MOVEMENT BASED ON DEEP LEARNING
Abstract: Emotion recognition from electroencephalogram (EEG) signals has gained significant attention due to its potential in human- computer interaction (HCI), mental health monitoring, and personalized content delivery. This paper presents the use of Convolutional neural networks (CNNs) to classify emotions such as happiness, sadness, fear, neutral, and disgust by leveraging a fusion of EEG signals and eye movements data. Compared to conventional methods of emotion detection, such as those that rely …
Published in International Journal of Optical Innovations & Research · Vol. 3, Issue 2, 2025 · pp. 8–15 Read article