Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification

• URL: http://arxiv.org/abs/2205.07203v1
• Date: Sun, 15 May 2022 07:13:33 GMT
• Title: Fused Deep Neural Network based Transfer Learning in Occluded Face Classification and Person re-Identification
• Authors: Mohamed Mohana, Prasanalakshmi B, Salem Alelyani, Mohammed Saleh Alsaqer
• Abstract summary: This paper aims to recognize the occlusion of one of four types in face images. Various transfer learning methods were tested, and the results show that MobileNet V2 with Gated Recurrent Unit(GRU) performs better than any other Transfer Learning methods.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent period of pandemic has brought person identification even with occluded face image a great importance with increased number of mask usage. This paper aims to recognize the occlusion of one of four types in face images. Various transfer learning methods were tested, and the results show that MobileNet V2 with Gated Recurrent Unit(GRU) performs better than any other Transfer Learning methods, with a perfect accuracy of 99% in classification of images as with or without occlusion and if with occlusion, then the type of occlusion. In parallel, identifying the Region of interest from the device captured image is done. This extracted Region of interest is utilised in face identification. Such a face identification process is done using the ResNet model with its Caffe implementation. To reduce the execution time, after the face occlusion type was recognized the person was searched to confirm their face image in the registered database. The face label of the person obtained from both simultaneous processes was verified for their matching score. If the matching score was above 90, the recognized label of the person was logged into a file with their name, type of mask, date, and time of recognition. MobileNetV2 is a lightweight framework which can also be used in embedded or IoT devices to perform real time detection and identification in suspicious areas of investigations using CCTV footages. When MobileNetV2 was combined with GRU, a reliable accuracy was obtained. The data provided in the paper belong to two categories, being either collected from Google Images for occlusion classification, face recognition, and facial landmarks, or collected in fieldwork. The motive behind this research is to identify and log person details which could serve surveillance activities in society-based e-governance.

Related papers

• Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method [77.65459419417533]
  We put face forgery in a semantic context and define that computational methods that alter semantic face attributes are sources of face forgery. We construct a large face forgery image dataset, where each image is associated with a set of labels organized in a hierarchical graph. We propose a semantics-oriented face forgery detection method that captures label relations and prioritizes the primary task.
  arXiv  Detail & Related papers  (2024-05-14T10:24:19Z)
• Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations [32.33331065408444]
  Existing deepfake detectors are not optimized for this detection task when an image is associated with a specific and identifiable individual. This study focuses on the deepfake detection of facial images of individual public figures. We demonstrate that the detection performance can be improved by exploiting the idempotency property of neural networks.
  arXiv  Detail & Related papers  (2023-12-13T10:21:00Z)
• Deep Learning based CNN Model for Classification and Detection of Individuals Wearing Face Mask [0.0]
  This project utilizes deep learning to create a model that can detect face masks in real-time streaming video as well as images. The primary focus of this research is to enhance security, particularly in sensitive areas. The research unfolds in three stages: image pre-processing, image cropping, and image classification.
  arXiv  Detail & Related papers  (2023-11-17T09:24:04Z)
• Exploring Decision-based Black-box Attacks on Face Forgery Detection [53.181920529225906]
  Face forgery generation technologies generate vivid faces, which have raised public concerns about security and privacy. Although face forgery detection has successfully distinguished fake faces, recent studies have demonstrated that face forgery detectors are very vulnerable to adversarial examples.
  arXiv  Detail & Related papers  (2023-10-18T14:49:54Z)
• FACE-AUDITOR: Data Auditing in Facial Recognition Systems [24.082527732931677]
  Few-shot-based facial recognition systems have gained increasing attention due to their scalability and ability to work with a few face images. To prevent the face images from being misused, one straightforward approach is to modify the raw face images before sharing them. We propose a complete toolkit FACE-AUDITOR that can query the few-shot-based facial recognition model and determine whether any of a user's face images is used in training the model.
  arXiv  Detail & Related papers  (2023-04-05T23:03:54Z)
• Towards Intrinsic Common Discriminative Features Learning for Face Forgery Detection using Adversarial Learning [59.548960057358435]
  We propose a novel method which utilizes adversarial learning to eliminate the negative effect of different forgery methods and facial identities. Our face forgery detection model learns to extract common discriminative features through eliminating the effect of forgery methods and facial identities.
  arXiv  Detail & Related papers  (2022-07-08T09:23:59Z)
• Multi-Dataset Benchmarks for Masked Identification using Contrastive Representation Learning [0.0]
  COVID-19 pandemic has drastically changed accepted norms globally. Official documents such as passports, driving license and national identity cards are enrolled with fully uncovered face images. In an airport or security checkpoint it is safer to match the unmasked image of the identifying document to the masked person rather than asking them to remove the mask. We propose a contrastive visual representation learning based pre-training workflow which is specialized to masked vs unmasked face matching.
  arXiv  Detail & Related papers  (2021-06-10T08:58:10Z)
• Facial Masks and Soft-Biometrics: Leveraging Face Recognition CNNs for Age and Gender Prediction on Mobile Ocular Images [53.913598771836924]
  We address the use of selfie ocular images captured with smartphones to estimate age and gender. We adapt two existing lightweight CNNs proposed in the context of the ImageNet Challenge. Some networks are further pre-trained for face recognition, for which very large training databases are available.
  arXiv  Detail & Related papers  (2021-03-31T01:48:29Z)
• DeepFake Detection Based on the Discrepancy Between the Face and its Context [94.47879216590813]
  We propose a method for detecting face swapping and other identity manipulations in single images. Our approach involves two networks: (i) a face identification network that considers the face region bounded by a tight semantic segmentation, and (ii) a context recognition network that considers the face context. We describe a method which uses the recognition signals from our two networks to detect such discrepancies. Our method achieves state of the art results on the FaceForensics++, Celeb-DF-v2, and DFDC benchmarks for face manipulation detection, and even generalizes to detect fakes produced by unseen methods.
  arXiv  Detail & Related papers  (2020-08-27T17:04:46Z)
• FMT:Fusing Multi-task Convolutional Neural Network for Person Search [33.91664470686695]
  We propose a fusing multi-task convolutional neural network(FMT-CNN) to tackle the correlation and heterogeneity of detection and re-identification. Experiment results on CUHK-SYSU Person Search dataset show that the performance of our proposed method is superior to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-03-01T05:20:47Z)
• Investigating the Impact of Inclusion in Face Recognition Training Data on Individual Face Identification [93.5538147928669]
  We audit ArcFace, a state-of-the-art, open source face recognition system, in a large-scale face identification experiment with more than one million distractor images. We find a Rank-1 face identification accuracy of 79.71% for individuals present in the model's training data and an accuracy of 75.73% for those not present.
  arXiv  Detail & Related papers  (2020-01-09T15:50:28Z)

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.