Face recognition based on convolutional neural network

Coşkun, Musab; Uçar, Ayşegül; Yıldırım, Özal; Demir, Yakup

doi:10.1109/mees.2017.8248937

Cited by 209 publications

(91 citation statements)

References 31 publications

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“…It does not suffer from overfitting, overcomes the limitation of other machine learning algorithms, and is very effective at reducing the parameters amount using dimensional reduction methods without affecting the quality of models. It is used to solve complex problems in different domains such as image classification and object detection due to their better performance [ 44 , 45 , 46 , 47 , 48 ]. In our model, the input image with size of 28 × 28 pixels goes through 8 convolution layers to produce 32, 64, 128, 256 feature maps using filters with a convolution kernel of a 3 × 3 receptive field.…”

Section: Proposed Stress Image-based Detection Modelmentioning

confidence: 99%

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images

Elzeiny

Qaraqe

2020

Sensors

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Stress is subjective and is manifested differently from one person to another. Thus, the performance of generic classification models that classify stress status is crude. Building a person-specific model leads to a reliable classification, but it requires the collection of new data to train a new model for every individual and needs periodic upgrades because stress is dynamic. In this paper, a new binary classification (called stressed and non-stressed) approach is proposed for a subject’s stress state in which the inter-beat intervals extracted from a photoplethysomogram (PPG) were transferred to spatial images and then to frequency domain images according to the number of consecutive. Then, the convolution neural network (CNN) was used to train and validate the classification accuracy of the person’s stress state. Three types of classification models were built: person-specific models, generic classification models, and calibrated-generic classification models. The average classification accuracies achieved by person-specific models using spatial images and frequency domain images were 99.9%, 100%, and 99.8%, and 99.68%, 98.97%, and 96.4% for the training, validation, and test, respectively. By combining 20% of the samples collected from test subjects into the training data, the calibrated generic models’ accuracy was improved and outperformed the generic performance across both the spatial and frequency domain images. The average classification accuracy of 99.6%, 99.9%, and 88.1%, and 99.2%, 97.4%, and 87.6% were obtained for the training set, validation set, and test set, respectively, using the calibrated generic classification-based method for the series of inter-beat interval (IBI) spatial and frequency domain images. The main contribution of this study is the use of the frequency domain images that are generated from the spatial domain images of the IBI extracted from the PPG signal to classify the stress state of the individual by building person-specific models and calibrated generic models.

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Section: Proposed Stress Image-based Detection Modelmentioning

confidence: 99%

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images

Elzeiny

Qaraqe

2020

Sensors

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“…where VLCP(.) is a function of VLCP and this function is defined in Equations (5)- (14), HLCP(.) is a function of HLCP, and this function is defined in Equations (15)- (24), B VLCP and B HLCP are the VLCP and HLCP applied images.…”

Section: Steps Of the Proposed Face Recognition Architecturementioning

confidence: 99%

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

et al. 2019

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In this study, a new face recognition architecture is proposed using fuzzy-based Discrete Wavelet Transform (DWT) and fuzzy with two novel local graph descriptors. These graph descriptors are called Local Cross Pattern (LCP). The proposed fuzzy wavelet-based face recognition architecture consists of DWT, Triangular fuzzy set transformation, and textural feature extraction with local descriptors and classification phases. Firstly, the LL (Low-Low) sub-band is obtained by applying the 2 Dimensions Discrete Wavelet Transform (2D DWT) to face images. After that, the triangular fuzzy transformation is applied to this band in order to obtain A, B, and C images. The proposed LCP is then applied to the B image. LCP consists of two types of descriptors: Vertical Local Cross Pattern (VLCP) and Horizontal Local Cross Pattern (HLCP). Linear discriminant analysis, quadratic discriminant, analysis, quadratic kernel-based support vector machine (QKSVM), and K-nearest neighbors (KNN) were ultimately used to classify the extracted features. Ten widely used descriptors in the literature are applied to the fuzzy wavelet architecture. AT&T, CIE, Face94, and FERET databases are used for performance evaluation of the proposed methods. Experimental results show that the LCP descriptors have high face recognition performance, and the fuzzy wavelet-based model significantly improves the performances of the textural descriptors-based face recognition methods. Moreover, the proposed fuzzy-based domain and LCP method achieved classification accuracy rates of 97.3%, 100.0%, 100.0%, and 96.3% for AT&T, CIE, Face94, and FERET datasets, respectively.

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“…LSTM networks are well suited for classification, processing, and predictions based on time-series data. The deep learning approach has shown a significant degree of success in various areas such as face identification [22], number and character recognition [23], and object classification [24]. We therefore designed a novel and robust indoor positioning system based on the deep learning technique that can accurately estimate the trajectory of a target under a dynamic indoor scenario.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

et al. 2019

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The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experiment results show that LSTM provides a significant improvement in the indoor positioning performance through the recognition task. The proposed system achieves a better localization performance than the trivial fingerprinting method, with an average error of 0.782 m in an indoor area of 128.6 m2. Additionally, the proposed system exhibited robust performance by excluding the abnormal activity from the pedestrian activities.

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Face recognition based on convolutional neural network

Cited by 209 publications

References 31 publications

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images

Stress Classification Using Photoplethysmogram-Based Spatial and Frequency Domain Images

Face Recognition with Triangular Fuzzy Set-Based Local Cross Patterns in Wavelet Domain

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

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