Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification

• URL: http://arxiv.org/abs/2303.13814v1
• Date: Fri, 24 Mar 2023 05:28:35 GMT
• Title: Multimodal Adaptive Fusion of Face and Gait Features using Keyless attention based Deep Neural Networks for Human Identification
• Authors: Ashwin Prakash, Thejaswin S, Athira Nambiar and Alexandre Bernardino
• Abstract summary: Soft biometrics such as gait are widely used with face in surveillance tasks like person recognition and re-identification. We propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks.
• Score: 67.64124512185087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Biometrics plays a significant role in vision-based surveillance applications. Soft biometrics such as gait is widely used with face in surveillance tasks like person recognition and re-identification. Nevertheless, in practical scenarios, classical fusion techniques respond poorly to changes in individual users and in the external environment. To this end, we propose a novel adaptive multi-biometric fusion strategy for the dynamic incorporation of gait and face biometric cues by leveraging keyless attention deep neural networks. Various external factors such as viewpoint and distance to the camera, are investigated in this study. Extensive experiments have shown superior performanceof the proposed model compared with the state-of-the-art model.

Related papers

• DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection [67.3143177137102]
  Deepfake detection refers to detecting artificially generated or edited faces in images or videos. We propose a novel Deepfake detection framework named DeepFidelity to adaptively distinguish real and fake faces.
  arXiv  Detail & Related papers  (2023-12-07T07:19:45Z)
• Efficient aggregation of face embeddings for decentralized face recognition deployments (extended version) [0.7349727826230862]
  Biometrics are one of the most privacy-sensitive data. Ubiquitous authentication systems with a focus on privacy favor decentralized approaches. This paper proposes an efficient way to aggregate embeddings used for face recognition based on an extensive analysis on different datasets.
  arXiv  Detail & Related papers  (2022-12-20T09:28:25Z)
• Multi-level Data Representation For Training Deep Helmholtz Machines [0.0]
  We guide the learning of a biologically plausible generative model called the Helmholtz Machine in complex search spaces using the Human Image Perception mechanism. We propose to overcome this problem, by providing the network's hidden layers with visual queues at different resolutions using a Multi-level Data representation. The results on several image datasets showed the model was able to not only obtain better overall quality but also a wider diversity in the generated images.
  arXiv  Detail & Related papers  (2022-10-26T16:55:40Z)
• Video Coding for Machine: Compact Visual Representation Compression for Intelligent Collaborative Analytics [101.35754364753409]
  Video Coding for Machines (VCM) is committed to bridging to an extent separate research tracks of video/image compression and feature compression. This paper summarizes VCM methodology and philosophy based on existing academia and industrial efforts.
  arXiv  Detail & Related papers  (2021-10-18T12:42:13Z)
• Neural Dependency Coding inspired Multimodal Fusion [11.182263394122142]
  Recent work in deep fusion models via neural networks has led to substantial improvements in areas like speech recognition, emotion recognition and analysis, captioning and image description. Inspired by recent neuroscience ideas about multisensory integration and processing, we investigate the effect of synergy maximizing loss functions.
  arXiv  Detail & Related papers  (2021-09-28T17:52:09Z)
• Deep Collaborative Multi-Modal Learning for Unsupervised Kinship Estimation [53.62256887837659]
  Kinship verification is a long-standing research challenge in computer vision. We propose a novel deep collaborative multi-modal learning (DCML) to integrate the underlying information presented in facial properties. Our DCML method is always superior to some state-of-the-art kinship verification methods.
  arXiv  Detail & Related papers  (2021-09-07T01:34:51Z)
• The FaceChannel: A Fast & Furious Deep Neural Network for Facial Expression Recognition [71.24825724518847]
  Current state-of-the-art models for automatic Facial Expression Recognition (FER) are based on very deep neural networks that are effective but rather expensive to train. We formalize the FaceChannel, a light-weight neural network that has much fewer parameters than common deep neural networks. We demonstrate how our model achieves a comparable, if not better, performance to the current state-of-the-art in FER.
  arXiv  Detail & Related papers  (2020-09-15T09:25:37Z)
• Bayesian optimization for automatic design of face stimuli [2.572404739180802]
  We propose a novel framework which combines generative networks (GANs) with Bayesian optimization to identify individual response patterns to many different faces. Formally, we employ Bayesian optimization to efficiently search the latent space of state-of-the-art GAN models, with the aim to automatically generate novel faces. We show how the algorithm can efficiently locate an individual's optimal face while mapping out their response across different semantic transformations of a face.
  arXiv  Detail & Related papers  (2020-07-20T10:27:18Z)
• Unsupervised Domain Adaptation in Person re-ID via k-Reciprocal Clustering and Large-Scale Heterogeneous Environment Synthesis [76.46004354572956]
  We introduce an unsupervised domain adaptation approach for person re-identification. Experimental results show that the proposed ktCUDA and SHRED approach achieves an average improvement of +5.7 mAP in re-identification performance.
  arXiv  Detail & Related papers  (2020-01-14T17:43:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.