Advances in Face Detection and Facial Image Analysis

Kawulok, Michał; Çelebi, Mehmet; Smołka, Bogdan

doi:10.1007/978-3-319-25958-1

Cited by 41 publications

(6 citation statements)

References 19 publications

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“…Subset of labeled faces in the wild (LFW) dataset (Learned-Miller et al, 2016), which was involved into the face identification protocol (Best-Rowden et al, 2014). C = 596 subjects who have at least two images in the LFW database and at least one video in the YouTube Faces (YTF) database (subjects in YTF are a subset of those in LFW) are used in all clustering methods.…”

Section: Facial Clusteringmentioning

confidence: 99%

“…Though the main purpose of this paper is face grouping in unsupervised environment, it is important to analyze the quality of face identification of the proposed model. Thus, in the last experiment of this subsection I deal with the LFW dataset (Learned-Miller et al, 2016) and PubFig83 dataset (Pinto et al, 2011). I took 9,164 photos of 1,680 persons from LFW with more than one photo.…”

Section: Facial Clusteringmentioning

confidence: 99%

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Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet

Savchenko

2019

PeerJ Computer Science

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This paper is focused on the automatic extraction of persons and their attributes (gender, year of born) from album of photos and videos. A two-stage approach is proposed in which, firstly, the convolutional neural network simultaneously predicts age/gender from all photos and additionally extracts facial representations suitable for face identification. Here the MobileNet is modified and is preliminarily trained to perform face recognition in order to additionally recognize age and gender. The age is estimated as the expected value of top predictions in the neural network. In the second stage of the proposed approach, extracted faces are grouped using hierarchical agglomerative clustering techniques. The birth year and gender of a person in each cluster are estimated using aggregation of predictions for individual photos. The proposed approach is implemented in an Android mobile application. It is experimentally demonstrated that the quality of facial clustering for the developed network is competitive with the state-of-the-art results achieved by deep neural networks, though implementation of the proposed approach is much computationally cheaper. Moreover, this approach is characterized by more accurate age/gender recognition when compared to the publicly available models.

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Section: Facial Clusteringmentioning

confidence: 99%

Section: Facial Clusteringmentioning

confidence: 99%

Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet

Savchenko

2019

PeerJ Computer Science

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“…The network architecture for the face recognition is based on the ResNet-34 network [16], with a few layers removed and the number of filters per layer reduced by half. In this application, it is used a previous trained network, which has an accuracy of 99.4% on the standard Labeled Faces in the Wild benchmark [23]. To create our dataset of features, each face is mapped by the previous trained neural network to the corresponding feature The data set used to train the network was constituted by some persons that are part of the Research Center in Digitalization and Intelligent Robotics (CeDRI).…”

Section: Face Recognitionmentioning

confidence: 99%

Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station

Variz

Piardi

Rodrigues

et al. 2019

2019 IEEE 17th International Conference on Industrial Informatics (INDIN)

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for all the time dispensed, by the orientations, critical analyses, motivation and confidence they have always transmitted along this path. To the IPB, mainly the professors and members from CeDRI for the support, companionship and availability in the accomplishment of the various tasks, by sharing their knowledge.To all my friends who have always support me, with friendship shown over the years.Good Luck to all.vii Abstract Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decentralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This thesis discusses the application of supervised Machine Learning (ML) techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the Quality Control (QC) of Human Machine Interface (HMI) consoles, equipped with pressure buttons and Liquid Crystal Display (LCD) displays. Machine learning techniques were applied for the recognition of the operator's face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.

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“…We train the network using a metric loss function over 3 million faces from the FaceScrub [15] and VGG-Face [17] datasets. This model is able to predict with 99.38% accuracy if two faces belong to the same individual on the Labeled Faces in the Wild (LFW) [11] dataset.…”

Section: Salient Facementioning

confidence: 99%

Real-time Video Summarization on Commodity Hardware

Taylor

Qureshi

2018

Proceedings of the 12th International Conference on Distributed Smart Cameras

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We present a method for creating video summaries in real-time on commodity hardware. Real-time here refers to the fact that the time required for video summarization is less than the duration of the input video. First, low-level features are use to discard undesirable frames. Next, video is divided into segments, and segment-level features are extracted for each segment. Tree-based models trained on widely available video summarization and computational aesthetics datasets are then used to rank individual segments, and top-ranked segments are selected to generate the final video summary. We evaluate the proposed method on The SumMe Video Summarization (SumMe) dataset and show that our method is able to achieve summarization accuracy that is comparable to that of a current state-of-the-art deep learning method, while posting significantly faster run-times. Our method on average is able to generate a video summary in time that is shorter than the duration of the video.

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Advances in Face Detection and Facial Image Analysis

Cited by 41 publications

References 19 publications

Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet

Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet

Machine Learning Applied to an Intelligent and Adaptive Robotic Inspection Station

Real-time Video Summarization on Commodity Hardware

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