23,446 research outputs found
Face Detection with the Faster R-CNN
The Faster R-CNN has recently demonstrated impressive results on various
object detection benchmarks. By training a Faster R-CNN model on the large
scale WIDER face dataset, we report state-of-the-art results on two widely used
face detection benchmarks, FDDB and the recently released IJB-A.Comment: technical repor
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
DETECTION OF A HUMAN HEAD ON A LOW-QUALITY IMAGE AND ITS SOFTWARE IMPLEMENTATION
The paper considers the task solution of detection on two-dimensional images not only face, but head of a human regardless of the turn to the observer. Such task is also complicated by the fact that the image receiving at the input of the recognition algorithm may be noisy or captured in low light conditions. The minimum size of a person’s head in an image to be detected for is 10 × 10 pixels. In the course of development, a dataset was prepared containing over 1000 labelled images of classrooms at BSTU n.a. V.G. Shukhov. The markup was carried out using a segmentation software tool specially developed by the authors. Three architectures of convolutional neural networks were trained for human head detection task: a fully convolutional neural network (FCN) with clustering, the Faster R-CNN architecture and the Mask R-CNN architecture. The third architecture works more than ten times slower than the first one, but it almost does not give false positives and has the precision and recall of head detection over 90% on both test and training samples. The Faster R-CNN architecture gives worse accuracy than Mask R-CNN, but it gives fewer false positives than FCN with clustering. Based on Mask R-CNN authors have developed software for human head detection on a lowquality image. It is two-level web-service with client and server modules. This software is used to detect and count people in the premises. The developed software works with IP cameras, which ensures its scalability for different practical computer vision applications
Face recognition using faster R-CNN with inception-V2 architecture for CCTV camera
Detection and prevention of criminal incidents using CCTV are currently increasing trend, for example, car and motorcycle parking lot. However, not continuous people monitoring and careless of events produce useless CCTV function for the prevention of criminal incidents. In this paper, face recognition is used for the recognition of vehicle owners in parking lots that are CCTV installed. The Faster-RCNN method is used for face detection and also for face recognition. Inception V2 architecture is utilized due to has a high accuracy among Convolutional Neural Network architecture. The best learning rate and epoch parameters for the Faster R-CNN model are optimized to improve face recognition on CCTV. In this research, the dataset consists of 6 people images with 50 faces images for each people, which used as training data, testing data, and validation data
FaceOff: Anonymizing Videos in the Operating Rooms
Video capture in the surgical operating room (OR) is increasingly possible
and has potential for use with computer assisted interventions (CAI), surgical
data science and within smart OR integration. Captured video innately carries
sensitive information that should not be completely visible in order to
preserve the patient's and the clinical teams' identities. When surgical video
streams are stored on a server, the videos must be anonymized prior to storage
if taken outside of the hospital. In this article, we describe how a deep
learning model, Faster R-CNN, can be used for this purpose and help to
anonymize video data captured in the OR. The model detects and blurs faces in
an effort to preserve anonymity. After testing an existing face detection
trained model, a new dataset tailored to the surgical environment, with faces
obstructed by surgical masks and caps, was collected for fine-tuning to achieve
higher face-detection rates in the OR. We also propose a temporal
regularisation kernel to improve recall rates. The fine-tuned model achieves a
face detection recall of 88.05 % and 93.45 % before and after applying
temporal-smoothing respectively.Comment: MICCAI 2018: OR 2.0 Context-Aware Operating Theater
CMS-RCNN: Contextual Multi-Scale Region-based CNN for Unconstrained Face Detection
Robust face detection in the wild is one of the ultimate components to
support various facial related problems, i.e. unconstrained face recognition,
facial periocular recognition, facial landmarking and pose estimation, facial
expression recognition, 3D facial model construction, etc. Although the face
detection problem has been intensely studied for decades with various
commercial applications, it still meets problems in some real-world scenarios
due to numerous challenges, e.g. heavy facial occlusions, extremely low
resolutions, strong illumination, exceptionally pose variations, image or video
compression artifacts, etc. In this paper, we present a face detection approach
named Contextual Multi-Scale Region-based Convolution Neural Network (CMS-RCNN)
to robustly solve the problems mentioned above. Similar to the region-based
CNNs, our proposed network consists of the region proposal component and the
region-of-interest (RoI) detection component. However, far apart of that
network, there are two main contributions in our proposed network that play a
significant role to achieve the state-of-the-art performance in face detection.
Firstly, the multi-scale information is grouped both in region proposal and RoI
detection to deal with tiny face regions. Secondly, our proposed network allows
explicit body contextual reasoning in the network inspired from the intuition
of human vision system. The proposed approach is benchmarked on two recent
challenging face detection databases, i.e. the WIDER FACE Dataset which
contains high degree of variability, as well as the Face Detection Dataset and
Benchmark (FDDB). The experimental results show that our proposed approach
trained on WIDER FACE Dataset outperforms strong baselines on WIDER FACE
Dataset by a large margin, and consistently achieves competitive results on
FDDB against the recent state-of-the-art face detection methods
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