Facial micro expressions are brief, spontaneous, and crucial emotions deep inside the mind, reflecting the actual thoughts for that moment. Humans can cover their emotions on a large scale, but their actual intentions and emotions can be extracted at a micro-level. Micro expressions are organic when compared with macro expressions, posing a challenge to both humans, as well as machines, to identify. In recent years, detection of facial expressions are widely used in commercial complexes, hotels, restaurants, psychology, security, offices, and education institutes. The aim and motivation of this paper are to provide an end-to-end architecture that accurately detects the actual expressions at the micro-scale features. However, the main research is to provide an analysis of the specific parts that are crucial for detecting the micro expressions from a face. Many states of the art approaches have been trained on the micro facial expressions and compared with our proposed Lossless Attention Residual Network (LARNet) approach. However, the main research on this is to provide analysis on the specific parts that are crucial for detecting the micro expressions from a face. Many CNN-based approaches extracts the features at local level which digs much deeper into the face pixels. However, the spatial and temporal information extracted from the face is encoded in LARNet for a feature fusion extraction on specific crucial locations, such as nose, cheeks, mouth, and eyes regions. LARNet outperforms the state-of-the-art methods with a slight margin by accurately detecting facial micro expressions in real-time. Lastly, the proposed LARNet becomes accurate and better by training with more annotated data.

Visual compatibility and virtual feel are critical metrics for fashion analysis yet are missing in existing fashion designs and platforms. An explicit model is much needed for implanting visual compatibility through fashion image inpainting and virtual try-on. With rapid advancements in the Computer Vision realm, the increase in creating customer experience which leads to the great potential of interest to retailers and customers. The public datasets available are very much fit for generating outfits from Generative Adversarial Networks (GANs) but the custom outfits of the users themselves lead to low accuracy levels. This work is the first step in analyzing and experimenting with the fit of custom outfits and visualizing it to the users on them which creates the great customer experience. The work analyses the need for providing visualization of custom outfits on users in the large corpora of AI in Fashion. The authors propose a novel architecture which facilitates the combining outfits provided by the retailers and visualize it on the users themselves using Neural Body Fit. This work creates a benchmark in disentangling the custom generation of cloth outfits using GANs and virtually trying it on the users to ensure a virtual-photorealistic appearance and results to create a great customer experience by using AI. Extensive experiments show the high accuracy levels on custom outfits generated by GANs but not in customized levels. This experiment creates new state-of-art results by plotting users pose for calculating the lengths of each body-part segment (hand, leg, and so forth), segmentation + NBF for accurate fitting of the cloth outfit. This paper is different from all other competitors in terms of approach for the virtual try-on for creating a new customer experience. INDEX TERMS Neural body fit, generative adversarial networks (GANs), pose, customer experience, segmentation.

In recent years, with the advancements in the Deep Learning realm, it has been easy to create and generate synthetically the face swaps from GANs and other tools, which are very realistic, leaving few traces which are unclassifiable by human eyes. These are known as 'DeepFakes' and most of them are anchored in video formats. Such realistic fake videos and images are used to create a ruckus and affect the quality of public discourse on sensitive issues; defaming one's profile, political distress, blackmailing and many more fake cyber terrorisms are envisioned. This work proposes a microscopic-typo comparison of video frames. This temporal-detection pipeline compares very minute visual traces on the faces of real and fake frames using Convolutional Neural Network (CNN) and stores the abnormal features for training. A total of 512 facial landmarks were extracted and compared. Parameters such as eye-blinking lip-synch; eyebrows movement, and position, are few main deciding factors that classify into real or counterfeit visual data. The Recurrent Neural Network (RNN) pipeline learns based on these features-fed inputs and then evaluates the visual data. The model was trained with the network of videos consisting of their real and fake, collected from multiple websites. The proposed algorithm and designed network set a new benchmark for detecting the visual counterfeits and show how this system can achieve competitive results on any fake generated video or image. INDEX TERMS DeepFakes, Generative Adversarial Network (GANs), Facial landmarks, Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), Visual Counterfeits.

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Contactless biometric systems have increased ever since the corona pandemic outbreak. The two main contactless biometric systems are facial recognition and gait patterns recognition. The authors in the previous work [11] have built hybrid architecture AccessNet. It involves combination of three systems: facial recognition, facial anti-spoofing, and gait recognition. This work involves deploying the hybrid architecture and deploying two individual systems such as facial recognition with facial anti-spoofing and gait recognition individually and comparing the individual results in real-time with the AccessNet hybrid architecture results. This work even involves in identifying the main crucial features from each system that are responsible for predicting a subject. It includes extracting few crucial parameters from gait recognition architecture, facial recognition and facial anti-spoof architectures by visualizing the hidden layers. Each individual method is trained and tested in real-time, which is deployed on both edge device NvidiaJetsonNano, and high-end GPU. A conclusion is also adapted in terms of commercial and research usage for each single method after analysing the real-time test results

Gait walking patterns are one of the key research topics in natural biometrics. The temporal information of the unique gait sequence of a person is preserved and used as a powerful data for access. Often there is a dive into the flexibility of gait sequence due to unstructured and unnecessary sequences that tail off the necessary sequence constraints. The authors in this work present a novel perspective, which extracts useful gait parameters regarded as independent frames and patterns. These patterns and parameters mark as unique signature for each subject in access authentication. This information extracted learns to identify the patterns associated to form a unique gait signature for each person based on their style, foot pressure, angle of walking, angle of bending, acceleration of walk, and step-by-step distance. These parameters form a unique pattern to plot under unique identity for access authorization. This sanitized data of patterns is further passed to a residual deep convolution network that automatically extracts the hierarchical features of gait pattern signatures. The end layer comprises of a Softmax classifier to classify the final prediction of the subject identity. This state-of-the-art work creates a gait-based access authentication that can be used in highly secured premises. This work was specially designed for Defence Department premises authentication. The authors have achieved an accuracy of 90 % ± 1.3 % in real time. This paper mainly focuses on the assessment of the crucial features of gait patterns and analysis of gait patterns research.