Efficient prediction of trait judgments from faces using deep neural networks

Judgments of people from their faces are often invalid but influence many social decisions (e.g., legal sentencing), making them an important target for automated prediction. Direct training of deep convolutional neural networks (DCNNs) is difficult because of sparse human ratings, but features obtained from DCNNs pre-trained on other classifications (e.g., object recognition) can predict trait judgments within a given face database. However, it remains unknown if this latter approach generalizes across faces, raters, or traits. Here we directly compare three distinct types of face features, and test them across multiple out-of-sample datasets and traits. DCNNs pre-trained on face identification provided features that generalized the best, and models trained to predict a given trait also predicted several other traits. We demonstrate the flexibility, generalizability, and efficiency of using DCNN features to predict human trait judgments from faces, providing an easily scalable framework for automated prediction of human judgment.

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A Cautionary Note on Predicting Social Judgments from Faces with Deep Neural Networks

Affective Science ◽

10.1007/s42761-021-00075-5 ◽

2021 ◽

Author(s):

Umit Keles ◽

Chujun Lin ◽

Ralph Adolphs

Keyword(s):

Neural Networks ◽

Object Recognition ◽

Face Identification ◽

Affective States ◽

Social Judgments ◽

Deep Convolutional Neural Networks ◽

Analysis Pipeline ◽

Cognitive States ◽

Social Decisions ◽

Out Of Sample

AbstractPeople spontaneously infer other people’s psychology from faces, encompassing inferences of their affective states, cognitive states, and stable traits such as personality. These judgments are known to be often invalid, but nonetheless bias many social decisions. Their importance and ubiquity have made them popular targets for automated prediction using deep convolutional neural networks (DCNNs). Here, we investigated the applicability of this approach: how well does it generalize, and what biases does it introduce? We compared three distinct sets of features (from a face identification DCNN, an object recognition DCNN, and using facial geometry), and tested their prediction across multiple out-of-sample datasets. Across judgments and datasets, features from both pre-trained DCNNs provided better predictions than did facial geometry. However, predictions using object recognition DCNN features were not robust to superficial cues (e.g., color and hair style). Importantly, predictions using face identification DCNN features were not specific: models trained to predict one social judgment (e.g., trustworthiness) also significantly predicted other social judgments (e.g., femininity and criminal), and at an even higher accuracy in some cases than predicting the judgment of interest (e.g., trustworthiness). Models trained to predict affective states (e.g., happy) also significantly predicted judgments of stable traits (e.g., sociable), and vice versa. Our analysis pipeline not only provides a flexible and efficient framework for predicting affective and social judgments from faces but also highlights the dangers of such automated predictions: correlated but unintended judgments can drive the predictions of the intended judgments.

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Double Additive Margin Softmax Loss for Face Recognition

Applied Sciences ◽

10.3390/app10010060 ◽

2019 ◽

Vol 10 (1) ◽

pp. 60 ◽

Cited By ~ 1

Author(s):

Shengwei Zhou ◽

Caikou Chen ◽

Guojiang Han ◽

Xielian Hou

Keyword(s):

Neural Networks ◽

Face Recognition ◽

Loss Function ◽

State Of The Art ◽

Feature Learning ◽

Loss Functions ◽

Deep Convolutional Neural Networks ◽

Large Margin ◽

Face Features ◽

Geometrical Explanation

Learning large-margin face features whose intra-class variance is small and inter-class diversity is one of important challenges in feature learning applying Deep Convolutional Neural Networks (DCNNs) for face recognition. Recently, an appealing line of research is to incorporate an angular margin in the original softmax loss functions for obtaining discriminative deep features during the training of DCNNs. In this paper we propose a novel loss function, termed as double additive margin Softmax loss (DAM-Softmax). The presented loss has a clearer geometrical explanation and can obtain highly discriminative features for face recognition. Extensive experimental evaluation of several recent state-of-the-art softmax loss functions are conducted on the relevant face recognition benchmarks, CASIA-Webface, LFW, CALFW, CPLFW, and CFP-FP. We show that the proposed loss function consistently outperforms the state-of-the-art.

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AUTOMATIC DIAGNOSIS OF BREAST CANCER IN HISTOLOGY IMAGES USING DEEP CONVOLUTIONAL NEURAL NETWORKS

KỶ YẾU HỘI NGHỊ KHOA HỌC CÔNG NGHỆ QUỐC GIA LẦN THỨ XI NGHIÊN CỨU CƠ BẢN VÀ ỨNG DỤNG CÔNG NGHỆ THÔNG TIN ◽

10.15625/vap.2018.0009 ◽

2018 ◽

Author(s):

Hung Le Minh ◽

Manh Mai Van ◽

Toan Tran Dinh ◽

Tot Tran Dac ◽

Tran Van Lang

Keyword(s):

Breast Cancer ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Automatic Diagnosis

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CNN-based Classification of Degraded Images

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-028 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 28-1-28-7 ◽

Cited By ~ 1

Author(s):

Kazuki Endo ◽

Masayuki Tanaka ◽

Masatoshi Okutomi

Keyword(s):

Neural Networks ◽

Image Restoration ◽

Image Classification ◽

Convolutional Neural Networks ◽

Deep Convolutional Neural Networks ◽

Alternative Approach ◽

Degraded Image ◽

Degraded Images ◽

Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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Deep Convolutional Neural Networks Approach for Classification of Lung Diseases using X-Rays: COVID-19, Pneumonia, and Tuberculosis

International Journal of Performability Engineering ◽

10.23940/ijpe.20.09.p2.13321340 ◽

2020 ◽

Vol 16 (9) ◽

pp. 1332

Author(s):

Patil Narayani ◽

Ingole Kalyani ◽

Mangala T. Rajani

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Lung Diseases ◽

X Rays ◽

Deep Convolutional Neural Networks

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Dorsal Hand Vein Identification Based on Deep Convolutional Neural Networks and Visualizing Intermediate Layer Activations

European Journal of Science and Technology ◽

10.31590/ejosat.713601 ◽

2020 ◽

pp. 512-521

Author(s):

Khaled ALASHIK ◽

Saleh HUSSIN ◽

Remzi YILDIRIM ◽

Abubaker ALGUTTAR

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Intermediate Layer ◽

Deep Convolutional Neural Networks ◽

Dorsal Hand ◽

Hand Vein ◽

Dorsal Hand Vein

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Deep convolutional neural networks for cardiovascular vulnerable plaque detection

MATEC Web of Conferences ◽

10.1051/matecconf/201927702024 ◽

2019 ◽

Vol 277 ◽

pp. 02024 ◽

Cited By ~ 1

Author(s):

Lincan Li ◽

Tong Jia ◽

Tianqi Meng ◽

Yizhe Liu

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Vulnerable Plaque ◽

Recall Rate ◽

Superior Performance ◽

Learning Approaches ◽

Deep Convolutional Neural Networks ◽

Vulnerable Plaques ◽

Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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