Environment
hassan Farsi; Mehrdad Rohani; Sajad Mohamadzadeh
Articles in Press, Accepted Manuscript, Available Online from 08 April 2024
Abstract
Facial feature recognition (FFR) has witnessed a remarkable surge in recent years, driven by its extensive applications in identity recognition, security, and intelligent imaging. The UTKFace dataset plays a pivotal role in advancing FFR by providing a rich dataset of facial images with accurate age, ...
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Facial feature recognition (FFR) has witnessed a remarkable surge in recent years, driven by its extensive applications in identity recognition, security, and intelligent imaging. The UTKFace dataset plays a pivotal role in advancing FFR by providing a rich dataset of facial images with accurate age, gender, and race labels. This paper proposes a novel multi-task learning (MTL) model that leverages the powerful Efficient-Net architecture and incorporates attention-based learning with two key innovations. First, we introduce an age-specific loss function that minimizes the impact of errors in less critical cases while focusing the learning process on accurate age estimation within sensitive age ranges. This innovation is trained using the UTKFace dataset and is specifically optimized to improve accuracy in age estimation across different age groups. Second, we present an enhanced attention mechanism that guides the model to prioritize features that contribute to more robust FFR. This mechanism is trained on the diverse and challenging images of UTKFace and is capable of identifying subtle and discriminative features in faces for more accurate gender, race, and age recognition. Furthermore, our proposed method achieves a 30% reduction in model parameters compared to the baseline network while maintaining accuracy. Extensive comparisons with existing state-of-the-art methods demonstrate the efficiency and effectiveness of our proposed approach. Using the UTKFace dataset as the evaluation benchmark, our model achieves a 0.62% improvement in gender recognition accuracy, a 2.35% improvement in race recognition accuracy, and a noteworthy 3.23-year reduction in mean absolute error for age estimation.
Environment
A. Sezavar; H. Farsi; S. Mohamadzadeh
Abstract
Person re-identification (re-id) is one of the most critical and challenging topics in image processing and artificial intelligence. In general, person re-identification means that a person seen in the field of view of one camera can be found and tracked by other non-overlapped cameras. Low-resolution ...
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Person re-identification (re-id) is one of the most critical and challenging topics in image processing and artificial intelligence. In general, person re-identification means that a person seen in the field of view of one camera can be found and tracked by other non-overlapped cameras. Low-resolution frames, high occlusion in crowded scene, and few samples for training supervised models make re-id challenging. This paper proposes a new model for person re-identification to overcome the noisy frames and extract robust features from each frame. To this end, a noise-aware system is implemented by training an auto-encoder on artificially damaged frames to overcome noise and occlusion. A model for person re-identification is implemented based on deep convolutional neural networks. Experimental results on two actual databases, CUHK01 and CUHK03, demonstrate that the proposed method performs better than state-of-the-art methods.
Environment
Z. Dorrani; H. Farsi; S. Mohamadzadeh
Abstract
In traffic monitoring for video analysis systems, vehicle shadows have a negative effect on their performance. Shadow detection and removal are essential steps in accurate vehicle detection. In this paper, a new method is proposed for shadow detection using a novel convolution neural network architecture. ...
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In traffic monitoring for video analysis systems, vehicle shadows have a negative effect on their performance. Shadow detection and removal are essential steps in accurate vehicle detection. In this paper, a new method is proposed for shadow detection using a novel convolution neural network architecture. In the proposed method, the edges of the image are first extracted. Edge extraction reduces calculation, and accelerates the execution of the method. The background of the frame is then removed and the main features are extracted using the ResUNet-a architecture. This architecture consists of two parts: the encoder and the decoder, which detect the shadow at the decoder output and then remove it. Deep learning is used to detect shadows, which increases the accuracy of the analysis. The ResUNet-a architecture can learn complex, hierarchical, and appropriate features from the image for accurate feature detection and discarding the irrelevant shadow, thereby outperforming conventional filters.The results show that the proposed method provides better performance on NJDOT traffic video, highway-1, and highway-3 datasets than popular shadow removal methods. Also, the method improves the evaluation criteria such as F-measure and runtime. The F-measure is 94 and 93% for highway-1 and highway-3, respectively.
