作者：
Guo, NanGu, KeQiao, JunfeiLiu, HantaoBeijing Univ Technol
Fac Informat TechnolBeijing Artificial Intellige Engn Res Ctr Intelligent Percept & Autonomous Con Minist EducBeijing Lab Smart Environm ProtectBe Beijing 100124 Peoples R China Cardiff Univ
Sch Comp Sci & Informat Cardiff CF10 3AT Wales
Convolutional neural networks (CNNs) can be generally regarded as learning-based visual systems for computer vision tasks. By imitating the operating mechanism of the human visual system (HVS), CNNs can even achieve b...
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Convolutional neural networks (CNNs) can be generally regarded as learning-based visual systems for computer vision tasks. By imitating the operating mechanism of the human visual system (HVS), CNNs can even achieve better results than human beings in some visual tasks. However, they are primary when compared to the HVS for the reason that the HVS has the ability of active vision to promptly analyze and adapt to specific tasks. In this article, a new unified pooling framework is proposed and a series of pooling methods are designed based on the framework to implement active vision to CNNs. In addition, an active selection pooling (ASP) is put forward to reorganize the existing and newly proposed pooling methods. The CNN models with an ASP tend to have a behavior of focus selection according to tasks during the training process, which acts extremely similar to the HVS.
Kinship verification from faces aims to determine whether two person share some family relationship based only on the visual facial patterns. This has attracted a significant interests among the scientific community d...
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Kinship verification from faces aims to determine whether two person share some family relationship based only on the visual facial patterns. This has attracted a significant interests among the scientific community due to its potential applications in social media mining and finding missing children. In this work, We propose a novel pattern analysis technique for kinship verification based on a new deeplearning-based approach. More specifically, given a pair of face images, we first use Resnet50 to extract deep features from each image. Then, feature distances between each pair of images are computed. Importantly, to overcome the problem of unbalanced data, One Hot Encoding for labels is utilised. The distances finally are fed to a deep neural networks to determine the kinship relation. Extensive experiments are conducted on FIW dataset containing 11 classes of kinship relationships. The experiments showed very promising results and pointed out the importance of balancing the training dataset. Moreover, our approach showed interesting ability of generalization. Results show that our approach performs better than all existing approaches on grandparents-grandchildren type of kinship. To support the principle of open and reproducible research, we are soon making our code publicly available to the research community: ***/Steven-HDQ/Kinship-Recognition.
In this paper, an approach for Facial Expressions Recognition (FER) based on a multi-facial patches (MFP) aggregation network is proposed. deep features are learned from facial patches using convolutional neural sub-n...
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In this paper, an approach for Facial Expressions Recognition (FER) based on a multi-facial patches (MFP) aggregation network is proposed. deep features are learned from facial patches using convolutional neural sub-networks and aggregated within one architecture for expression classification. Besides, a framework based on two data augmentation techniques is proposed to expand FER labels training datasets. Consequently, the proposed shallow convolutional neural networks (CNN) based approach does not need large datasets for training. The proposed framework is evaluated on three FER datasets. Results show that the proposed approach achieves state-of-art FER deeplearning approaches performance when the model is trained and tested on images from the same dataset. Moreover, the proposed data augmentation techniques improve the expression recognition rate, and thus can be a solution for training deeplearning FER models using small datasets. The accuracy degrades significantly when testing for dataset bias. A fine-tuning can overcome the problem of transition from laboratory-controlled conditions to in-the-wild conditions. Finally, the emotional face is mapped using the MFP-CNN and the contribution of the different facial areas in displaying emotion as well as their importance in the recognition of each facial expression are studied.
Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label ...
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ISBN:
(纸本)9781665441155
Image classification methods are usually trained to perform predictions taking into account a predefined group of known classes. Real-world problems, however, may not allow for a full knowledge of the input and label spaces, making failures in recognition a hazard to deep visual learning. Open set recognition methods are characterized by the ability to correctly identifying inputs of known and unknown classes. In this context, we propose GeMOS: simple and plug-and-play open set recognition modules that can be attached to pre-trained deep Neural Networks for visual recognition. The GeMOS framework pairs pre-trained Convolutional Neural Networks with generative models for open set recognition to extract open set scores for each sample, allowing for failure recognition in object recognition tasks. We conduct a thorough evaluation of the proposed method in comparison with state-of-the-art open set algorithms, finding that GeMOS either outperforms or is statistically indistinguishable from more complex and costly models.
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