20 research outputs found
LT3 at SemEval-2020 Task 8 : multi-modal multi-task learning for memotion analysis
Internet memes have become a very popular mode of expression on social media networks today.
Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging
research object for automatic analysis. In this paper, we describe our contribution to the SemEval2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which
incorporates “memebeddings”, viz. joint text and vision features, to learn and optimize for all
three Memotion subtasks simultaneously. The experimental results show that the proposed system
constantly outperforms the competition’s baseline, and the system setup with continual learning
(where tasks are trained sequentially) obtains the best classification F1-scores
Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments
Real-world face recognition applications often deal with suboptimal image
quality or resolution due to different capturing conditions such as various
subject-to-camera distances, poor camera settings, or motion blur. This
characteristic has an unignorable effect on performance. Recent
cross-resolution face recognition approaches used simple, arbitrary, and
unrealistic down- and up-scaling techniques to measure robustness against
real-world edge-cases in image quality. Thus, we propose a new standardized
benchmark dataset and evaluation protocol derived from the famous Labeled Faces
in the Wild (LFW). In contrast to previous derivatives, which focus on pose,
age, similarity, and adversarial attacks, our Cross-Quality Labeled Faces in
the Wild (XQLFW) maximizes the quality difference. It contains only more
realistic synthetically degraded images when necessary. Our proposed dataset is
then used to further investigate the influence of image quality on several
state-of-the-art approaches. With XQLFW, we show that these models perform
differently in cross-quality cases, and hence, the generalizing capability is
not accurately predicted by their performance on LFW. Additionally, we report
baseline accuracy with recent deep learning models explicitly trained for
cross-resolution applications and evaluate the susceptibility to image quality.
To encourage further research in cross-resolution face recognition and incite
the assessment of image quality robustness, we publish the database and code
for evaluation.Comment: 9 pages, 4 figures, 2 table
Exploring Factors for Improving Low Resolution Face Recognition
State-of-the-art deep face recognition approaches report near perfect
performance on popular benchmarks, e.g., Labeled Faces in the Wild. However,
their performance deteriorates significantly when they are applied on low
quality images, such as those acquired by surveillance cameras. A further
challenge for low resolution face recognition for surveillance applications is
the matching of recorded low resolution probe face images with high resolution
reference images, which could be the case in watchlist scenarios. In this
paper, we have addressed these problems and investigated the factors that would
contribute to the identification performance of the state-of-the-art deep face
recognition models when they are applied to low resolution face recognition
under mismatched conditions. We have observed that the following factors affect
performance in a positive way: appearance variety and resolution distribution
of the training dataset, resolution matching between the gallery and probe
images, and the amount of information included in the probe images. By
leveraging this information, we have utilized deep face models trained on
MS-Celeb-1M and fine-tuned on VGGFace2 dataset and achieved state-of-the-art
accuracies on the SCFace and ICB-RW benchmarks, even without using any training
data from the datasets of these benchmarks.Comment: CVPR Workshop on Biometrics 201
Human Face Recognition and Age Estimation with Machine Learning: A Critical Review and Future Perspective
Face Recognition (FR) applications are becoming more and more common these days. Face recognition, techniques, tools, and performance are all shown in this work, along with a literature review and gaps in many areas. Some of the most common uses of the FR include medical and government sectors as well as educational institutions. The FR technique can identify an appropriate individual through a camera. Online courses, online FDPs, and Webinars are becoming more interactive nowadays. Using Machine Learning, it is possible to quickly and securely determine a student\u27s unique id to administer virtual online tests. The paper is an analysis of Machine learning and deep learning algorithms as well as tools such as Matlab and Python. The paper covers a survey of different aspects such as face detection, face recognition, face expressions, and age estimation. Hence, this is helpful for researchers to choose the right direction for their research. Future face recognition research is also considered in the paper which is now trending in face recognition systems. Data from recent years are used to evaluate the performance
Meet-in-the-middle: Multi-scale upsampling and matching for cross-resolution face recognition
In this paper, we aim to address the large domain gap between high-resolution
face images, e.g., from professional portrait photography, and low-quality
surveillance images, e.g., from security cameras. Establishing an identity
match between disparate sources like this is a classical surveillance face
identification scenario, which continues to be a challenging problem for modern
face recognition techniques. To that end, we propose a method that combines
face super-resolution, resolution matching, and multi-scale template
accumulation to reliably recognize faces from long-range surveillance footage,
including from low quality sources. The proposed approach does not require
training or fine-tuning on the target dataset of real surveillance images.
Extensive experiments show that our proposed method is able to outperform even
existing methods fine-tuned to the SCFace dataset
RESDEN: A Novel Deep Unified Model for Face Recognition System
The Face Recognition technology plays a significant role in the field of Computer Vision in contemporary times. The research article is centered on a Facial attendance system that utilizes a deep learning technique to recognize face photos. To execute face identification and classification via the use of deep learning processes, many Convolutional Neural Network (CNN) models are taken into account. Previous studies have mostly focused on either the ResNet or DenseNet-based convolutional neural network model. The present research utilizes the merging of ResNet and DenseNet to propose a hybrid model. The proposed work is expected to provide enhanced efficiency and accuracy. In the training and testing stages of the simulation, considerations are made for both binary and category classifications. The current research focuses on the use of the LFW dataset. The pictures undergo an initial step of the noise reduction process. The evaluation of picture quality is conducted by taking into account metrics such as Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM). After the proposed model has undergone training, it generates photographs of superior quality. Finally, the proposed system incorporates the RESDEN framework, which integrates DenseNet with a noise reduction technique, a segmentation mechanism, and a CNN based on ResNet. A comparative analysis has been conducted to evaluate the accuracy of several filtered picture sets across different convolutional neural network (CNN) models. The simulation results indicate that the suggested model exhibited a good level of performance and accuracy
Extremely Low Quality Image Face Recognition
Aastate jooksul on piltide töötlemine ja analüüs arenenud pakkudes nüüd igapäevastele väljakutsetele praktilisi lahendusi. Uute lahenduste ja ettepanekute sünd toob kaasa ka uusi väljakutseid, mis on paratamatult seotud innovaatiliste uuendustega. Olemasolevad näotuvastuse algoritmid on hästi toiminud ja neid on muu hulgas rakendatud sellistes lahendustes nagu sotsiaalmeedia kujutise märgistamine, mobiiltelefoni näo biomeetriline autentimine ja sisserände piirikontrolli näotuvastus. Põhjus miks need algoritmid on suutnud eelnimetatud stsenaariumides hästi toimida tuleneb sellest, et kasutuskõlblike kujutiste kvaliteet on tavaliselt kõrge eraldusvõimega [1].Teistes näidetes kus näotuvastus vajalikuks osutub nagu linna turvakaamerad, lennujaama kaamerad ja muud situatsioonid kus kujutise salvestuskvaliteeti ei saa kontrollida või manipuleerida, muutub jõulisema lahenduse leidmine pea kohustuslikuks, et oleks võimalik nägu tuvastada sõltumata kaadri suurusest, valgusoludest, rassist, vanusest, kehaasendist või muudest varieeruvatest faktoritest, mis võivad oluliselt muuta algoritmide võimet kujutistest aru saada.Käesoleva töö eesmärk on tuvastada ja testida alternatiivseid meetodeid näotuvastusülesannete täitmiseks äärmiselt madala kvaliteediga piltides.Image processing and analysis have evolved over the years into providing practical solutions to everyday challenges. The birth of new solutions and proposals also create new challenges usually surrounding the new innovations.Existing face recognition algorithms have performed well and they have been deployed into solutions such as social media image tagging, mobile phone facial bio-metric authentication, immigration border control face matching among other solutions. The existing algorithms have been able to perform well in these scenarios because of the quality of the image from these use cases are usually of high quality with high resolution (HR) [1]. In other possible application of face recognition such as city camera surveillance, airport security surveillance and other related scenarios where image stream quality cannot be directly controlled or manipulated, it becomes imperative to seek a more robust solution that can deal with face recognition regardless of the frame size, lighting condition, race, age, pose and other varying factors that can significantly change the way the images are perceived by existing algorithms.The goal of this thesis is to identify and test alternative methods of performing face recognition task in extremely low-quality images