This work presents a method for estimating human facial attractiveness, based on supervised learning techniques. Numerous facial features that describe facial geometry, color and texture, combined with an average human attractiveness score for each facial image, are used to train various predictors. Facial attractiveness ratings produced by the final predictor are found to be highly correlated ...

Face detection methods have relied on face datasets for training. However, existing face datasets tend to be in small scales for face learning in both constrained and unconstrained environments. In this paper, we first introduce our large-scale image datasets, Large-scale Labeled Face (LSLF) and noisy Large-scale Labeled Non-face (LSLNF). Our LSLF dataset consists of a large number of unconstra...

Current face or object detection methods via convolutional neural network (such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features based on an image pyramid. However, such a strategy increases the computational burden for face detection. In this paper, we propose a fast face detection method based on discriminative complete features (DCFs) extracted by an elaborately desig...

conclusions this study has provided some preliminary evidence of the reliability and validity of the persian version of rcas when used with family caregivers of older adults with dementia. background caregivers’ self-assessments of the care they provide are the main vehicles that help explore their experiences and are thought to have a major role in care outcomes. the rising number of people wi...

We present a method to narrow down the search space for scale-invariant human face detection, which uses Dynamic Attention Map implemented by Ising dynamics. Combining the proposed method and the scale-invariant face detection method which is based on both Higher-Order Local Autocorrelation (HLAC) features of Log-Polar image and Linear Discriminant Analysis for \face" and \not face" classiicati...

This paper presents an integrated approach to unconstrained face recognition in arbitrary scenes. The front end of the system comprises of a scale and pose tolerant face detector. Scale normalization is achieved through novel combination of a skin color segmentation and log-polar mapping procedure. Principal component analysis is used with the multi-view approach proposed in [10] to handle the ...

This paper presents a scale and rotation invariant face detection system. The system employs a hierarchical neural network, called SICoNNet, whose processing elements are governed by the nonlinear mechanism of shunting inhibition. The neural network is used as a face/nonface classifier that can handle in-plane rotated patterns. To train the network as a rotation invariant face classifier, an en...

3D face recognition is a promising alternative to face the problem of recognizing 2D robustness. Therefore, the main advantage of 3D face recognition-based approach uses all the information on the geometry of the face, which allows us to get an accurate representation of the face. In the proposing contribution all distinctive facial features are captured by first extracting SIFT (Scale Invarian...

This research paper deals with the implementation of face recognition using neural network (recognition classifier) on multi-scale features of face (such as eyes, nose, mouth and remaining portions of face). The proposed system contains three parts, preprocessing, multi scale feature extraction and face classification using neural network. The basic idea of the proposed method is to construct f...