668 research outputs found
Automatic human face detection for content-based image annotation
In this paper, an automatic human face detection approach using colour analysis is applied for content-based image annotation. In the face detection, the probable face region is detected by adaptive boosting algorithm, and then combined with a colour filtering classifier to enhance the accuracy in face detection. The initial experimental benchmark shows the proposed scheme can be efficiently applied for image annotation with higher fidelity
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
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
2D Face Recognition System Based on Selected Gabor Filters and Linear Discriminant Analysis LDA
We present a new approach for face recognition system. The method is based on
2D face image features using subset of non-correlated and Orthogonal Gabor
Filters instead of using the whole Gabor Filter Bank, then compressing the
output feature vector using Linear Discriminant Analysis (LDA). The face image
has been enhanced using multi stage image processing technique to normalize it
and compensate for illumination variation. Experimental results show that the
proposed system is effective for both dimension reduction and good recognition
performance when compared to the complete Gabor filter bank. The system has
been tested using CASIA, ORL and Cropped YaleB 2D face images Databases and
achieved average recognition rate of 98.9 %
Detecting Curved Objects Against Cluttered Backgrounds
Detecting curved objects against cluttered backgrounds is a hard problem in computer vision. We present new low-level and mid-level features to function in these environments. The low-level features are fast to compute, because they employ an integral image approach, which makes them especially useful in real-time applications. The mid-level features are built from low-level features, and are optimized for curved object detection. The usefulness of these features is tested by designing an object detection algorithm using these features. Object detection is accomplished by transforming the mid-level features into weak classifiers, which then produce a strong classifier using AdaBoost. The resulting strong classifier is then tested on the problem of detecting heads with shoulders. On a database of over 500 images of people, cropped to contain head and shoulders, and with a diverse set of backgrounds, the detection rate is 90% while the false positive rate on a database of 500 negative images is less than 2%
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