475,186 research outputs found
AUTOMATIC FACE RECOGNITION BASED ON LEARNING TO RANK FOR IMAGE QUALITY ASSESSMENT
Automatic face recognition technology has attracted a great amount of attention from both academia and industry in the recent trends. It is usually possible for practical recognition systems to capture multiple face images from each subject. Selecting face images with high quality for recognition is a promising stratagem for improving the system performance. We propose a simple and flexible framework for face image quality assessment, in which multiple feature fusion and learning to rank are used. The proposed method is simple and can adapt to different recognition methods. To demonstrate the overall effectiveness of the proposed method, we use heuristic criteria for data selection in our experiments
Generative Adversarial Networks for Improving Face Classification
Master's thesis Information- and communication technology IKT590 - University of Agder 2017Facial recognition can be applied in a wide variety of cases, including entertainment
purposes and biometric security. In this thesis we take a look at improving
the results of an existing facial recognition approach by utilizing generative adversarial
networks to improve the existing dataset.
The training data was taken from the LFW dataset[4] and was preprocessed
using OpenCV[2] for face detection. The faces in the dataset was cropped and
resized so every image is the same size and can easily be passed to a convolutional
neural network. To the best of our knowledge no generative adversarial network
approach has been applied to facial recognition by generating training data for
classification with convolutional neural networks.
The proposed approach to improving face classification accuracy is not improving
the classification algorithm itself but rather improving the dataset by generating
more data. In this thesis we attempt to use generative adversarial networks
to generate new data. We achieve an impressive accuracy of 99.42% with 3
classes, which is an improvement of 1.74% compared to not generating any new
data
Secure Face and Liveness Detection with Criminal Identification for Security Systems
The advancement of computer vision, machine learning, and image processing techniques has opened new avenues for enhancing security systems. In this research work focuses on developing a robust and secure framework for face and liveness detection with criminal identification, specifically designed for security systems. Machine learning algorithms and image processing techniques are employed for accurate face detection and liveness verification. Advanced facial recognition methods are utilized for criminal identification. The framework incorporates ML technology to ensure data integrity and identification techniques for security system. Experimental evaluations demonstrate the system's effectiveness in detecting faces, verifying liveness, and identifying potential criminals. The proposed framework has the potential to enhance security systems, providing reliable and secure face and liveness detection for improved safety and security.
The accuracy of the algorithm is 94.30 percent. The accuracy of the model is satisfactory even after the results are acquired by combining our rules inwritten by humans with conventional machine learning classification algorithms. Still, there is scope for improving and accurately classifying the attack precisely
Evaluating the Emotional State of a User Using a Webcam
In online learning is more difficult for teachers identify to see how individual students behave. Student’s emotions like self-esteem, motivation, commitment, and others that are believed to be determinant in student’s performance can not be ignored, as they are known (affective states and also learning styles) to greatly influence student’s learning. The ability of the computer to evaluate the emotional state of the user is getting bigger attention. By evaluating the emotional state, there is an attempt to overcome the barrier between man and non-emotional machine. Recognition of a real time emotion in e-learning by using webcams is research area in the last decade. Improving learning through webcams and microphones offers relevant feedback based upon learner’s facial expressions and verbalizations. The majority of current software does not work in real time – scans face and progressively evaluates its features. The designed software works by the use neural networks in real time which enable to apply the software into various fields of our lives and thus actively influence its quality. Validation of face emotion recognition software was annotated by using various experts. These expert findings were contrasted with the software results. An overall accuracy of our software based on the requested emotions and the recognized emotions is 78%. Online evaluation of emotions is an appropriate technology for enhancing the quality and efficacy of e-learning by including the learner´s emotional states
Review of Face Detection Systems Based Artificial Neural Networks Algorithms
Face detection is one of the most relevant applications of image processing
and biometric systems. Artificial neural networks (ANN) have been used in the
field of image processing and pattern recognition. There is lack of literature
surveys which give overview about the studies and researches related to the
using of ANN in face detection. Therefore, this research includes a general
review of face detection studies and systems which based on different ANN
approaches and algorithms. The strengths and limitations of these literature
studies and systems were included also.Comment: 16 pages, 12 figures, 1 table, IJMA Journa
Baseline CNN structure analysis for facial expression recognition
We present a baseline convolutional neural network (CNN) structure and image
preprocessing methodology to improve facial expression recognition algorithm
using CNN. To analyze the most efficient network structure, we investigated
four network structures that are known to show good performance in facial
expression recognition. Moreover, we also investigated the effect of input
image preprocessing methods. Five types of data input (raw, histogram
equalization, isotropic smoothing, diffusion-based normalization, difference of
Gaussian) were tested, and the accuracy was compared. We trained 20 different
CNN models (4 networks x 5 data input types) and verified the performance of
each network with test images from five different databases. The experiment
result showed that a three-layer structure consisting of a simple convolutional
and a max pooling layer with histogram equalization image input was the most
efficient. We describe the detailed training procedure and analyze the result
of the test accuracy based on considerable observation.Comment: 6 pages, RO-MAN2016 Conferenc
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