• Title, Summary, Keyword: DCNN

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Development of Combined Architecture of Multiple Deep Convolutional Neural Networks for Improving Video Face Identification (비디오 얼굴 식별 성능개선을 위한 다중 심층합성곱신경망 결합 구조 개발)

  • Kim, Kyeong Tae;Choi, Jae Young
    • Journal of Korea Multimedia Society
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    • v.22 no.6
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    • pp.655-664
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    • 2019
  • In this paper, we propose a novel way of combining multiple deep convolutional neural network (DCNN) architectures which work well for accurate video face identification by adopting a serial combination of 3D and 2D DCNNs. The proposed method first divides an input video sequence (to be recognized) into a number of sub-video sequences. The resulting sub-video sequences are used as input to the 3D DCNN so as to obtain the class-confidence scores for a given input video sequence by considering both temporal and spatial face feature characteristics of input video sequence. The class-confidence scores obtained from corresponding sub-video sequences is combined by forming our proposed class-confidence matrix. The resulting class-confidence matrix is then used as an input for learning 2D DCNN learning which is serially linked to 3D DCNN. Finally, fine-tuned, serially combined DCNN framework is applied for recognizing the identity present in a given test video sequence. To verify the effectiveness of our proposed method, extensive and comparative experiments have been conducted to evaluate our method on COX face databases with their standard face identification protocols. Experimental results showed that our method can achieve better or comparable identification rate compared to other state-of-the-art video FR methods.

A Recommendation Model based on Character-level Deep Convolution Neural Network (문자 수준 딥 컨볼루션 신경망 기반 추천 모델)

  • Ji, JiaQi;Chung, Yeongjee
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.23 no.3
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    • pp.237-246
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    • 2019
  • In order to improve the accuracy of the rating prediction of the recommendation model, not only user-item rating data are used but also consider auxiliary information of item such as comments, tags, or descriptions. The traditional approaches use a word-level model of the bag-of-words for the auxiliary information. This model, however, cannot utilize the auxiliary information effectively, which leads to shallow understanding of auxiliary information. Convolution neural network (CNN) can capture and extract feature vector from auxiliary information effectively. Thus, this paper proposes character-level deep-Convolution Neural Network based matrix factorization (Char-DCNN-MF) that integrates deep CNN into matrix factorization for a novel recommendation model. Char-DCNN-MF can deeper understand auxiliary information and further enhance recommendation performance. Experiments are performed on three different real data sets, and the results show that Char-DCNN-MF performs significantly better than other comparative models.

Anthropomorphic Animal Face Masking using Deep Convolutional Neural Network based Animal Face Classification

  • Khan, Rafiul Hasan;Lee, Youngsuk;Lee, Suk-Hwan;Kwon, Oh-Jun;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.22 no.5
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    • pp.558-572
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    • 2019
  • Anthropomorphism is the attribution of human traits, emotions, or intentions to non-human entities. Anthropomorphic animal face masking is the process by which human characteristics are plotted on the animal kind. In this research, we are proposing a compact system which finds the resemblance between a human face and animal face using Deep Convolutional Neural Network (DCNN) and later applies morphism between them. The whole process is done by firstly finding which animal most resembles the particular human face through a DCNN based animal face classification. And secondly, doing triangulation based morphing between the particular human face and the most resembled animal face. Compared to the conventional manual Control Point Selection system using an animator, we are proposing a Viola-Jones algorithm based Control Point selection process which detects facial features for the human face and takes the Control Points automatically. To initiate our approach, we built our own dataset containing ten thousand animal faces and a fourteen layer DCNN. The simulation results firstly demonstrate that the accuracy of our proposed DCNN architecture outperforms the related methods for the animal face classification. Secondly, the proposed morphing method manages to complete the morphing process with less deformation and without any human assistance.

An Action Unit co-occurrence constraint 3DCNN based Action Unit recognition approach

  • Jia, Xibin;Li, Weiting;Wang, Yuechen;Hong, SungChan;Su, Xing
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.14 no.3
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    • pp.924-942
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    • 2020
  • The facial expression is diverse and various among persons due to the impact of the psychology factor. Whilst the facial action is comparatively steady because of the fixedness of the anatomic structure. Therefore, to improve performance of the action unit recognition will facilitate the facial expression recognition and provide profound basis for the mental state analysis, etc. However, it still a challenge job and recognition accuracy rate is limited, because the muscle movements around the face are tiny and the facial actions are not obvious accordingly. Taking account of the moving of muscles impact each other when person express their emotion, we propose to make full use of co-occurrence relationship among action units (AUs) in this paper. Considering the dynamic characteristic of AUs as well, we adopt the 3D Convolutional Neural Network(3DCNN) as base framework and proposed to recognize multiple action units around brows, nose and mouth specially contributing in the emotion expression with putting their co-occurrence relationships as constrain. The experiments have been conducted on a typical public dataset CASME and its variant CASME2 dataset. The experiment results show that our proposed AU co-occurrence constraint 3DCNN based AU recognition approach outperforms current approaches and demonstrate the effectiveness of taking use of AUs relationship in AU recognition.

Toward Optimal FPGA Implementation of Deep Convolutional Neural Networks for Handwritten Hangul Character Recognition

  • Park, Hanwool;Yoo, Yechan;Park, Yoonjin;Lee, Changdae;Lee, Hakkyung;Kim, Injung;Yi, Kang
    • Journal of Computing Science and Engineering
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    • v.12 no.1
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    • pp.24-35
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    • 2018
  • Deep convolutional neural network (DCNN) is an advanced technology in image recognition. Because of extreme computing resource requirements, DCNN implementation with software alone cannot achieve real-time requirement. Therefore, the need to implement DCNN accelerator hardware is increasing. In this paper, we present a field programmable gate array (FPGA)-based hardware accelerator design of DCNN targeting handwritten Hangul character recognition application. Also, we present design optimization techniques in SDAccel environments for searching the optimal FPGA design space. The techniques we used include memory access optimization and computing unit parallelism, and data conversion. We achieved about 11.19 ms recognition time per character with Xilinx FPGA accelerator. Our design optimization was performed with Xilinx HLS and SDAccel environment targeting Kintex XCKU115 FPGA from Xilinx. Our design outperforms CPU in terms of energy efficiency (the number of samples per unit energy) by 5.88 times, and GPGPU in terms of energy efficiency by 5 times. We expect the research results will be an alternative to GPGPU solution for real-time applications, especially in data centers or server farms where energy consumption is a critical problem.

Animal Face Classification using Dual Deep Convolutional Neural Network

  • Khan, Rafiul Hasan;Kang, Kyung-Won;Lim, Seon-Ja;Youn, Sung-Dae;Kwon, Oh-Jun;Lee, Suk-Hwan;Kwon, Ki-Ryong
    • Journal of Korea Multimedia Society
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    • v.23 no.4
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    • pp.525-538
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    • 2020
  • A practical animal face classification system that classifies animals in image and video data is considered as a pivotal topic in machine learning. In this research, we are proposing a novel method of fully connected dual Deep Convolutional Neural Network (DCNN), which extracts and analyzes image features on a large scale. With the inclusion of the state of the art Batch Normalization layer and Exponential Linear Unit (ELU) layer, our proposed DCNN has gained the capability of analyzing a large amount of dataset as well as extracting more features than before. For this research, we have built our dataset containing ten thousand animal faces of ten animal classes and a dual DCNN. The significance of our network is that it has four sets of convolutional functions that work laterally with each other. We used a relatively small amount of batch size and a large number of iteration to mitigate overfitting during the training session. We have also used image augmentation to vary the shapes of the training images for the better learning process. The results demonstrate that, with an accuracy rate of 92.0%, the proposed DCNN outruns its counterparts while causing less computing costs.

Repeated Cropping based on Deep Learning for Photo Re-composition (사진 구도 개선을 위한 딥러닝 기반 반복적 크롭핑)

  • Hong, Eunbin;Jeon, Junho;Lee, Seungyong
    • Journal of KIISE
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    • v.43 no.12
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    • pp.1356-1364
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    • 2016
  • This paper proposes a novel aesthetic photo recomposition method using a deep convolutional neural network (DCNN). Previous recomposition approaches define the aesthetic score of photo composition based on the distribution of salient objects, and enhance the photo composition by maximizing the score. These methods suffer from heavy computational overheads, and often fail to enhance the composition because their optimization depends on the performance of existing salient object detection algorithms. Unlike previous approaches, we address the photo recomposition problem by utilizing DCNN, which shows remarkable performance in object detection and recognition. DCNN is used to iteratively predict cropping directions for a given photo, thus generating an aesthetically enhanced photo in terms of composition. Experimental results and user study show that the proposed framework can automatically crop the photo to follow specific composition guidelines, such as the rule of thirds.

Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks

  • Naseer, Sheraz;Saleem, Yasir
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.12 no.10
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    • pp.5159-5178
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    • 2018
  • Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.

Predicting Employment Earning using Deep Convolutional Neural Networks (딥 컨볼루션 신경망을 이용한 고용 소득 예측)

  • Ramadhani, Adyan Marendra;Kim, Na-Rang;Choi, Hyung-Rim
    • Journal of Digital Convergence
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    • v.16 no.6
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    • pp.151-161
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    • 2018
  • Income is a vital aspect of economic life. Knowing what their income will help people create budgets that allow them to pay for their living expenses. Income data is used by banks, stores, and service companies for marketing purposes and for retaining loyal customers; it is a crucial demographic element used at a wide variety of customer touch points. Therefore, it is essential to be able to make income predictions for existing and potential customers. This paper aims to predict employment earnings or income based on history, and uses machine learning techniques such as SVMs (Support Vector Machines), Gaussian, decision tree and DCNNs (Deep Convolutional Neural Networks) for predicting employment earnings. The results show that the DCNN method provides optimum results with 88% compared to other machine learning techniques used in this paper. Improvement of the data length such PCA has the potential to provide more optimum result.