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Face privacy concerns revolve around the ethical, social, and technological implications of collecting, storing, and using facial data. With the advancement of deep learning techniques, realistic face privacy involves techniques that obscure or alter facial features effectively without compromising the usability or quality of the visual content. Modern face privacy techniques suffer from three main problems: 1) lack of human perception, 2) indistinguishability, and 3) loss of facial attributes. Modern face privacy techniques generate random, realistic faces to conceal the identifiable features of the original faces but lack the application of human perception to face de-identification. Indistinguishability arises with the highly realistic nature of fake faces used in face privacy, making it difficult to distinguish whether a face has been manipulated. Most face-privacy methods also fails to retain the facial attributes of the de-identified faces. Our face de-identification method is designed to address all three issues mentioned. We propose a novel face de-identification method that considers both human perception and face recognition models when de-identifying a face. We explore the tradeoff between a user misidentifying the original identity with a well-known celebrity and a facial recognition model that tries to identify the original identity. We generate caricature faces of the de-identified faces to ensure our manipulated faces can be distinguished effortlessly. The face caricatures are the exaggeration of the eyes and mouth region, and we provide different exaggeration scales depending on preference and application. We perform an attribute preservation optimization process to retrieve all the facial attributes. We demonstrate our method through a series of both qualitative and quantitative experiments with numerous user studies.

Autoencoders (AEs) are central to hyperspectral anomaly detection, given their impressive efficacy. However, the current methodologies often neglect the global pixel similarity of the hyperspectral image (HsI), thereby limiting reconstruction accuracy. This study introduces an innovative pixel-associated AE approach that leverages pixel associations to augment hyperspectral anomaly detection. First, a dictionary construction methodology was introduced based on superpixel distance estimation to construct distinct dictionaries for background and local anomalies. Second, to recognize pixel similarities, the similarity metric of each pixel from the original HsI to the background dictionary and to the local anomaly dictionary was employed as the AE network input in lieu of the original HsI. Third, a dual hidden-layer feature similarity constraint network was proposed to enhance the reconstruction error of background and anomaly targets. Finally, the reconstruction error was utilized to score the anomaly target. The proposed method was benchmarked against other state-of-the-art techniques using synthetic and real HsI datasets to assess its effectiveness. The experimental results demonstrated the superior performance of the proposed method, outperforming the alternatives.

Caricature is an exaggerated form of artistic portraiture that accentuates unique yet subtle characteristics of human faces. Recently, advancements in deep end-to-end techniques have yielded encouraging outcomes in capturing both style and elevated exaggerations in creating face caricatures. Most of these approaches tend to produce cartoon-like results that could be more practical for real-world applications. In this study, we proposed a high-quality, unpaired face caricature method that is appropriate for use in the real world and uses computer vision techniques and GAN models. We attain the exaggeration of facial features and the stylization of appearance through a two-step process: Face caricature generation and face caricature projection. The face caricature generation step creates new caricature face datasets from real images and trains a generative model using the real and newly created caricature datasets. The Face caricature projection employs an encoder trained with real and caricature faces with the pre-trained generator to project real and caricature faces. Using the encoder and generator’s latent space, we perform an incremental facial exaggeration from the real image to the caricature faces. Our projection preserves the facial identity, attributes, and expressions from the input image. Also, it accounts for facial occlusions, such as reading glasses or sunglasses, to enhance the robustness of our model. Furthermore, we comprehensively compared our approach with various state-of-the-art face caricature methods, highlighting our process’s distinctiveness and exceptional realism.

Early childhood learning is an important concern for the concept building of children. Students have been taught with traditional methods from the beginning. However, these methods lack kids’ attention. Application of Augmented Reality (AR) in a traditional way of teaching can help students in better engagement and better learning. In this research, an ABCD-AR mobile based application is designed to overcome the flaws in the traditional education system. While designing AR mobile applications, certain guidelines need to be considered that cause problems for kids in their learning. These may include but are not limited to small screen size and irregularity. Considering these problems, this study proposes 23 usability principles for AR based learning applications for kids. These identified usability principles are validated by human experts. Furthermore, experiments are carried out to evaluate the application in terms of efficiency, effectiveness, learn-ability, user satisfaction & engagement. The results implies that the developed game-based application is highly effective and efficient because the mean of marks obtained by students before using ABCD-AR application is lower (M = 4.97, SD = 1.650) than the marks obtained after using ABCD-AR application (M = 7.97, SD = 1.608). Similarly, results indicate that mean of time taken to perform tests without using ABCD-AR application (M = 7.97, SD = 1.650) is higher as compared to with using it (M = 4.37, SD = 1.608). Majority of the students were entertained by playing the game while learning, and felt motivated to continue based on the game’s scenario due to the variety of activities included. Teachers and parents involved in these experiments have shown satisfaction towards the use of ABCD-AR application in learning process of preschool kids.

Template based learning, particularly Siamese networks, has recently become popular due to balancing accuracy and speed. However, preserving tracker robustness against challenging scenarios with real-time speed is a primary concern for visual object tracking. Siamese trackers confront difficulties handling target appearance changes continually due to less discrimination ability learning between target and background information. This paper presents stacked channel-spatial attention within Siamese networks to improve tracker robustness without sacrificing fast-tracking speed. The proposed channel attention strengthens target-specific channels increasing their weight while reducing the importance of irrelevant channels with lower weights. Spatial attention is focusing on the most informative region of the target feature map. We integrate the proposed channel and spatial attention modules to enhance tracking performance with end-to-end learning. The proposed tracking framework learns what and where to highlight important target information for efficient tracking. Experimental results on widely used OTB100, OTB50, VOT2016, VOT2017/18, TC-128, and UAV123 benchmarks verified the proposed tracker achieved outstanding performance compared with state-of-the-art trackers.

Background: The global burden on mental disorders have become too advanced with serious consequences on health and society. Over the years, depression, stress, and anxiety have become more common symptoms which affect all over population pyramid irrespective of the gender. Mental disorders are second most health indicator that causes morbidity. Objectives: The mental disorders affect the entire population and both genders equally. Present study was conducted to carry out meta-analysis study to highlight the prevalence of anxiety, depression, and stress among university students. Methodology: Various search engines i.e. Google scholar, Duckduckgo, AOL, Baidu, Yahoo as well as books and newsletter were used to collect the data. Results: Despite tremendous development in the era of health and education, there is significant prevalence (79.5%) of anxiety, depression, and stress in medical students that leads to morbidity and poor mental health disorders among these students. Conclusion: It is concluded from meta-analysis study that prevalence of depression and anxiety is alarmingly high. There is a need of the hour to develop and design the mental health prevention programs and health education programs to overcome this issue.

CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.

We propose to improve the visual object tracking by introducing a soft mask based low-level feature fusion technique. The proposed technique is further strengthened by integrating channel and spatial attention mechanisms. The proposed approach is integrated within a Siamese framework to demonstrate its effectiveness for visual object tracking. The proposed soft mask is used to give more importance to the target regions as compared to the other regions to enable effective target feature representation and to increase discriminative power. The low-level feature fusion improves the tracker robustness against distractors. The channel attention is used to identify more discriminative channels for better target representation. The spatial attention complements the soft mask based approach to better localize the target objects in challenging tracking scenarios. We evaluated our proposed approach over five publicly available benchmark datasets and performed extensive comparisons with 39 state-of-the-art tracking algorithms. The proposed tracker demonstrates excellent performance compared to the existing state-of-the-art trackers.

Many automobile companies and researchers have developed various safety systems to reduce fatalities by traffic accidents. In order to prevent traffic accidents by distracted driving, therefore, we present the vehicle and pedestrian detection using a novel image representation called equi-height mosaicking system. Furthermore, we additionally suggest the part-based side detection method using equi-height peripheral mosaicking image to detect approaching vehicles while driving