Face Sketch Recognition Using Computer Vision

Now-a-days need for technologies for identification, detection and recognition of suspects has increased. One of the most common biometric techniques is face recognition, since face is the convenient way used by the people to identify each-other. Understanding how humans recognize face sketches drawn by artists is of significant value to both criminal investigators and forensic researchers in Computer Vision. However, studies say that hand-drawn face sketches are still very limited in terms of artists and number of sketches because after any incident a forensic artist prepares a victim’s sketches on behalf of the description provided by an eyewitness. Sometimes suspect uses special mask to hide some common features of faces like nose, eyes, lips, face-color etc. but the outliner features of face biometrics one could never hide. Here we concentrate on some specific facial geometric feature which could be used to calculate some ratio of similarities from the template photograph database against the forensic sketches. The project describes the design of a system for face sketch recognition by a computer vision approach like Discrete Cosine Transform (DCT), Local Binary Pattern Histogram (LBPH) algorithm and a supervised machine learning model called Support Vector Machine (SVM) for face recognition. Tkinter is the standard GUI library for Python. Python when combined with Tkinter provides a fast and easy way to create GUI applications. Tkinter provides a powerful object-oriented interface to the Tk GUI toolkit.

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Pengenalan Wajah Manusia berbasis Algoritma Local Binary Pattern

Emitor: Jurnal Teknik Elektro ◽

10.23917/emitor.v17i2.6232 ◽

2017 ◽

Vol 17 (2) ◽

pp. 29-38

Author(s):

Ratih Purwati ◽

Gunawan Ariyanto

Keyword(s):

Computer Vision ◽

Support Vector Machine ◽

Face Recognition ◽

Local Binary Pattern ◽

Cross Validation ◽

Support Vector ◽

Fold Cross Validation

Face Recognition merupakan teknologi komputer untuk mengidentifikasi wajah manusia melalui gambar digital yang tersimpan di database. Wajah manusia dapat berubah bentuk sesuai dengan ekspresi yang dimilikinya. Wajah manusia dapat berubah bentuk sesuai dengan eskpresi yang dimilikinya. Ekspresi wajah manusia memiliki kemiripan satu sama lain sehingga untuk mengenali suatu ekspresi adalah kepunyaan siapa akan sedikit sulit. Pengenalan wajah terus menjadi topik aktif di zaman sekarang pada penelitian bidang computer vision. Penggunaan wajah manusia sering kita jumpai pada fitur-fitur aplikasi media sosial seperti Snapchat, Snapgram dari Instagram dan banyak aplikasi sosial media lainnya yang menggunakan teknologi tersebut. Pada penelitian ini dilakukan analisa pengenalan ekpresi wajah manusia dengan pendekatan fitur alogaritma Local Binary Pattern dan mencari pengembangan alogaritma dasar Local Binary Pattern yang paling optimal dengan cara menggabungkan metode Hisogram Equalization, Support Vector Machine, dan K-fold cross validation sehingga dapat meningkatkan pengenalan gambar wajah manusia pada hasil yang terbaik. Penelitian ini menginput beberapa database wajah manusia seperti JAFFE yang merupakan gambar wajah manusia wanita jepang yang berjumlah 10 orang dengan 7 ekspresi emosional seperti marah, sedih, bahagia, jijik, kaget, takut dan netral ke dalam sistem. YALE yaitu merupakan gambar wajah manusia orang Amerika. Serta menggunakan dataset CALTECH yang merupakan gambar manusia yang terdiri dari 450 gambar dengan ukuran 896 x 592 piksel dan disimpan dalam format JPEG. Kemudian data tersebut di sesuaikan dengan bentuk tekstur wajah masing-masing. Dari hasil penggabungan ketiga metode diatas dan percobaan-percobaan yang sudah dilakukan, didapatkan hasil yang paling optimal dalam pengenalan wajah manusia yaitu menggunakan dataset JAFFE dengan resolusi 92 x 112 piksel dan dengan tingkat penggunaan processor yang tinggi dapat mempengaruhi waktu kecepatan komputasi dalam proses menjalankan sistem sehingga menghasilkan prediksi yang lebih tepat.

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In-situ identification of material batches using machine learning for machining operations

Journal of Intelligent Manufacturing ◽

10.1007/s10845-020-01718-3 ◽

2020 ◽

Author(s):

Benjamin Lutz ◽

Dominik Kisskalt ◽

Andreas Mayr ◽

Daniel Regulin ◽

Matteo Pantano ◽

...

Keyword(s):

Machine Learning ◽

Prediction Accuracy ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Model ◽

Multiple Classification ◽

Subtractive Manufacturing ◽

Smart Service ◽

Machining Operations

AbstractIn subtractive manufacturing, differences in machinability among batches of the same material can be observed. Ignoring these deviations can potentially reduce product quality and increase manufacturing costs. To consider the influence of the material batch in process optimization models, the batch needs to be efficiently identified. Thus, a smart service is proposed for in-situ material batch identification. This service is driven by a supervised machine learning model, which analyzes the signals of the machine’s control, especially torque data, for batch classification. The proposed approach is validated by cutting experiments with five different batches of the same specified material at various cutting conditions. Using this data, multiple classification models are trained and optimized. It is shown that the investigated batches can be correctly identified with close to 90% prediction accuracy using machine learning. Out of all the investigated algorithms, the best results are achieved using a Support Vector Machine with 89.0% prediction accuracy for individual batches and 98.9% while combining batches of similar machinability.

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Development of the "smart library" application using the Intel Distribution of OpenVINO toolkit

10.30987/graphicon-2019-1-122-125 ◽

2019 ◽

Author(s):

Евгений Васильев ◽

Evgeniy Vasil'ev ◽

Валентина Кустикова ◽

Valentina Kustikova ◽

Иван Вихрев ◽

...

Keyword(s):

Machine Learning ◽

Computer Vision ◽

Deep Learning ◽

Face Recognition ◽

Source Code ◽

Face Features ◽

Machine Learning Model ◽

Library Users ◽

The Face

We represent a case study of using deep learning and computer vision library - the Intel Distribution of OpenVINO toolkit. We develop the automated “smart library” using DL and computer vision methods implemented in OpenVINO toolkit. The application involves the registration of the reader (adding information and photos of the new user); updating the machine learning model that describes the face features of the library users; authorization of the reader through face recognition; receiving and returning books by comparing the cover image with the database of flat images available in the library of books. The source code of the application is free available on GitHub: https://github.com/itlab-vision/openvino-smart-library. The developed application is planned to be published as a sample of the OpenVINO toolkit.

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Exploring U.S. Shifts in Anti-Asian Sentiment with the Emergence of COVID-19

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17197032 ◽

2020 ◽

Vol 17 (19) ◽

pp. 7032

Author(s):

Thu T. Nguyen ◽

Shaniece Criss ◽

Pallavi Dwivedi ◽

Dina Huang ◽

Jessica Keralis ◽

...

Keyword(s):

Machine Learning ◽

Application Programming Interface ◽

Learning Model ◽

Systematic Investigation ◽

Supervised Machine Learning ◽

Support Vector ◽

Time Period ◽

Machine Learning Model ◽

Life Impact ◽

Application Programming

Background: Anecdotal reports suggest a rise in anti-Asian racial attitudes and discrimination in response to COVID-19. Racism can have significant social, economic, and health impacts, but there has been little systematic investigation of increases in anti-Asian prejudice. Methods: We utilized Twitter’s Streaming Application Programming Interface (API) to collect 3,377,295 U.S. race-related tweets from November 2019–June 2020. Sentiment analysis was performed using support vector machine (SVM), a supervised machine learning model. Accuracy for identifying negative sentiments, comparing the machine learning model to manually labeled tweets was 91%. We investigated changes in racial sentiment before and following the emergence of COVID-19. Results: The proportion of negative tweets referencing Asians increased by 68.4% (from 9.79% in November to 16.49% in March). In contrast, the proportion of negative tweets referencing other racial/ethnic minorities (Blacks and Latinx) remained relatively stable during this time period, declining less than 1% for tweets referencing Blacks and increasing by 2% for tweets referencing Latinx. Common themes that emerged during the content analysis of a random subsample of 3300 tweets included: racism and blame (20%), anti-racism (20%), and daily life impact (27%). Conclusion: Social media data can be used to provide timely information to investigate shifts in area-level racial sentiment.

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FoRex Trading Using Supervised Machine Learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.23024 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 400 ◽

Cited By ~ 1

Author(s):

Thuy Nguyen Thi Thu ◽

Vuong Dang Xuan

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Exchange Rate ◽

Learning Algorithm ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Support Vector ◽

Machine Learning Algorithm ◽

Machine Learning Model

The exchange rate of each money pair can be predicted by using machine learning algorithm during classification process. With the help of supervised machine learning model, the predicted uptrend or downtrend of FoRex rate might help traders to have right decision on FoRex transactions. The installation of machine learning algorithms in the FoRex trading online market can automatically make the transactions of buying/selling. All the transactions in the experiment are performed by using scripts added-on in transaction application. The capital, profits results of use support vector machine (SVM) models are higher than the normal one (without use of SVM).

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Computer Vision Based Local Fruit Recognition

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a9789.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 2810-2820

Keyword(s):

Computer Vision ◽

Future Research ◽

Support Vector ◽

Tropical Fruits ◽

Monsoon Climate ◽

The People ◽

Vector Machines ◽

Tropical Monsoon ◽

Day By Day

Bangladesh is an agricultural country having a tropical monsoon climate. A large variety of tropical and sub-tropical fruits abound in Bangladesh. People of Bangladesh are fruit-lovers too. Currently, most of the people of this country are failing to recognize many of the rare local fruits and the number of this portion of people is increasing day by day. Thus, not only the natural heritage but also good sources of food are being diminished. Performing a machine vision based recognition of these fruits can help people recognize them. In this paper, we perform an in-depth exploration of a computer vision approach for recognizing rare local fruits of Bangladesh. A number of rare local fruits are classified based on the features extracted from their images. For our experiment, we have used a total of 480 images of 6 rare local fruits. We perform some preprocessing on the captured image and then expected features are extracted using image segmentation. Classification of the fruits is accomplished using support vector machines (SVMs). We have achieved 94.79% classification accuracy, which is not only good but also promising for future research.

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A Machine Learning Model for Predicting Unscheduled 72 h Return Visits to the Emergency Department by Patients with Abdominal Pain

Diagnostics ◽

10.3390/diagnostics12010082 ◽

2021 ◽

Vol 12 (1) ◽

pp. 82

Author(s):

Chun-Chuan Hsu ◽

Cheng-CJ Chu ◽

Ching-Heng Lin ◽

Chien-Hsiung Huang ◽

Chip-Jin Ng ◽

...

Keyword(s):

Machine Learning ◽

Emergency Department ◽

Abdominal Pain ◽

Learning Model ◽

Supervised Machine Learning ◽

Support Vector ◽

Research Database ◽

Machine Learning Model ◽

Extreme Gradient Boosting ◽

Return Visits

Seventy-two-hour unscheduled return visits (URVs) by emergency department patients are a key clinical index for evaluating the quality of care in emergency departments (EDs). This study aimed to develop a machine learning model to predict 72 h URVs for ED patients with abdominal pain. Electronic health records data were collected from the Chang Gung Research Database (CGRD) for 25,151 ED visits by patients with abdominal pain and a total of 617 features were used for analysis. We used supervised machine learning models, namely logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC), to predict URVs. The VC model achieved more favorable overall performance than other models (AUROC: 0.74; 95% confidence interval (CI), 0.69–0.76; sensitivity, 0.39; specificity, 0.89; F1 score, 0.25). The reduced VC model achieved comparable performance (AUROC: 0.72; 95% CI, 0.69–0.74) to the full models using all clinical features. The VC model exhibited the most favorable performance in predicting 72 h URVs for patients with abdominal pain, both for all-features and reduced-features models. Application of the VC model in the clinical setting after validation may help physicians to make accurate decisions and decrease URVs.

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Predicting Mechanical Properties of 3D Printed Lattice Structures

10.1115/detc2021-70249 ◽

2021 ◽

Author(s):

Shuai Ma ◽

Qian Tang ◽

Ying Liu ◽

Qixiang Feng

Keyword(s):

Machine Learning ◽

Mechanical Properties ◽

3D Printing ◽

Unit Length ◽

3D Models ◽

Design Stage ◽

Geometric Feature ◽

Support Vector ◽

Lattice Structures ◽

Machine Learning Model

Abstract Lattice structures (LS) manufactured by 3D printing are widely applied in many areas, such as aerospace and tissue engineering, due to their lightweight and adjustable mechanical properties. It is necessary to reduce costs by predicting the mechanical properties of LS at the design stage since 3D printing is exorbitant at present. However, predicting mechanical properties quickly and accurately poses a challenge. To address this problem, this study proposes a novel method that is applied to different LS and materials to predict their mechanical properties through machine learning. First, this study voxelised 3D models of the LS units and then calculated the entropy vector of each model as the geometric feature of the LS units. Next, the porosity, material density, elastic modulus, and unit length of the lattice unit are combined with entropy as the inputs of the machine learning model. The sample set includes 57 samples collected from previous studies. Support vector regression was used in this study to predict the mechanical properties. The results indicate that the proposed method can predict the mechanical properties of LS effectively and is suitable for different LS and materials. The significance of this work is that it provides a method with great potential to promote the design process of lattice structures by predicting their mechanical properties quickly and effectively.

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Machine Learning Assisted Chimera and Solitary States in Networks

Frontiers in Physics ◽

10.3389/fphy.2021.513969 ◽

2021 ◽

Vol 9 ◽

Author(s):

Niraj Kushwaha ◽

Naveen Kumar Mendola ◽

Saptarshi Ghosh ◽

Ajay Deep Kachhvah ◽

Sarika Jalan

Keyword(s):

Machine Learning ◽

Single Layer ◽

Dynamical Evolution ◽

Network Models ◽

Learning Model ◽

Supervised Machine Learning ◽

Support Vector ◽

Svm Classifier ◽

Chimera States ◽

Machine Learning Model

Chimera and Solitary states have captivated scientists and engineers due to their peculiar dynamical states corresponding to co-existence of coherent and incoherent dynamical evolution in coupled units in various natural and artificial systems. It has been further demonstrated that such states can be engineered in systems of coupled oscillators by suitable implementation of communication delays. Here, using supervised machine learning, we predict (a) the precise value of delay which is sufficient for engineering chimera and solitary states for a given set of system's parameters, as well as (b) the intensity of incoherence for such engineered states. Ergo, using few initial data points we generate a machine learning model which can then create a more refined phase plot as well as by including new parameter values. We demonstrate our results for two different examples consisting of single layer and multi layer networks. First, the chimera states (solitary states) are engineered by establishing delays in the neighboring links of a node (the interlayer links) in a 2-D lattice (multiplex network) of oscillators. Then, different machine learning classifiers, K-nearest neighbors (KNN), support vector machine (SVM) and multi-layer perceptron neural network (MLP-NN) are employed by feeding the data obtained from the network models. Once a machine learning model is trained using the limited amount of data, it predicts the precise value of critical delay as well as the intensity of incoherence for a given unknown systems parameters values. Testing accuracy, sensitivity, and specificity analysis reveal that MLP-NN classifier is better suited than Knn or SVM classifier for the predictions of parameters values for engineered chimera and solitary states. The technique provides an easy methodology to predict critical delay values as well as intensity of incoherence for that delay value for designing an experimental setup to create solitary and chimera states.

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Efficient Attendance Management System Based on Facial Recognition

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.12.16180 ◽

2018 ◽

Vol 7 (3.12) ◽

pp. 565 ◽

Cited By ~ 1

Author(s):

Setu Choudhary ◽

Aayush Kakaji ◽

Kusuma Pranay ◽

P Prabhu

Keyword(s):

Computer Vision ◽

Support Vector Machine ◽

Face Recognition ◽

Data Base ◽

Management System ◽

Facial Recognition ◽

Recognition System ◽

Support Vector ◽

Time Consumption ◽

Face Recognition System

A Face recognition system is an application of computer vision which is capable of performing two major tasks identifying and verifying a person from given data base. The objective of this paper is to design an effective attendance system which is based on facial recognition and intend to reduce the manual efforts of the teacher. In the conventional attendance system there are several issue like fake attendance, time consumption, manipulation of attendance. The algorithm used is named fisher face algorithm, which is already in use but it gives an accuracy of 5-6% and the amount of faces it can detect is comparatively less, Here we intend to use fisher face algorithm with the help of support vector machine(SVM). The system is trained with database faces. The data gets updated in the portal which is accessed by the faculty and the students. This paper is a speculative model of attendance management system using facial recognition.

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