Transfer learning for face recognition using fingerprint biometrics Rupali Kute, Vibha Vyas, Alwin Anuse Journal of King Saud University Engineering Sciences, 2026 Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect. Biometrics is a set of highly automated methods used for recognition purposes including analyzing physical traits of people and statistically measuring them. In forensic applications, fingerprint and face images are mostly used for recognition. There is a need during a criminal investigation to find out a face image of a person from its fingerprint. The proposed method able to identify the face of a person using an associated fingerprint. In this paper, Bregman divergence regularization is used to learn and optimize transferring subspace. This method first gleans knowledge from samples that are meant for training and transfer it to the testing samples. This regularization helps to minimize the probability distribution differences between two different domains. It helps to find a common subspace that boosts the performance of independent and identically distributed (i.i.d.) condition of samples. Practically the samples violate this i.i.d. condition. However, it will help to identify the correct suspect.
The Conglomerate Compression Approach for Visual Pattern Recognition Networks in Edge AI Applications Seema Bhalgaonkar, Mousami Munot, Alwin Anuse 2023 IEEE Pune Section International Conference Punecon 2023, 2023 Deep Neural Networks have shown remarkable performance in various applications of Visual Pattern Recognition (VPR). This field continues to grow due to emergence of new architectures, availability of huge data, and powerful computing resources. However, because of their high computational complexity that subsequently demands higher memory requirements, and power consumption, the deployment of such DNN-based Visual Pattern Recognition Networks (VPRN) models in edgeAI applications is hindered. Such overheads necessitate the compression of VPRN models while retaining their performance. This paper explores multiple compression techniques which combines Knowledge Distillation (KD), pruning and quantization that are applied to DNNs to achieve higher compression ratio.
Explainable AI for Reliable Detection of Cyberbullying Vaishali U. Gongane, Mousami V. Munot, Alwin Anuse 2023 IEEE Pune Section International Conference Punecon 2023, 2023 The advent of Internet has brought a pivotal revolution in communication technology. This revolution is observed through tremendous use of devices like cell phones and social networking websites that provide a platform for people to interact virtually. The use of social networking websites has provided economical, educational and societal benefits. The ease of access to information and the liberty to openly share and publish information on social networking sites also has a downside. Past decade has witnessed an upsurge of objectionable and cyberbullying content shared on social media. The massiveness and the sensitive bullying content shared on social media platforms make manual method of detecting such content practically challenging. Automated approaches like Artificial Intelligence (AI) based techniques are widely being adopted to detect and moderate online cyberbullying content. Natural Language Processing (NLP) and Deep Learning (DL) are at forefront in automating the detection of cyberbullying content. Inspite of the wide adoption of DL models for detection of bullying content, the decision and predictions made by these models are difficult to comprehend. Explainable AI (XAI) is a promising field that provide interpretations to the decision made by DL models. Local Interpretable Model-Agnostic Explanations (LIME) XAI technique provide better explanations by highlighting the most pertinent features that contributed to model's decision. This paper proposes a unified BiLSTM-LIME model for multiclass classification of cyberbullying content on Twitter platform.
Deep Learning Approach for Traffic Sign Recognition 13th International Conference on Advances in Computing Control and Telecommunication Technologies Act 2022, 2022
