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Constrained Local Models (CLMs) are a well-established family of methods for facial landmark detection. However, they have recently fallen out of favor to cascaded regressionbased approaches. This is in part due to the inability of existing CLM local detectors to model the very complex individual landmark appearance that is affected by expression, illumination, facial hair, makeup, and accessories...
We present a framework for robust face detection and landmark localisation of faces in the wild, which has been evaluated as part of `the 2nd Facial Landmark Localisation Competition'. The framework has four stages: face detection, bounding box aggregation, pose estimation and landmark localisation. To achieve a high detection rate, we use two publicly available CNN-based face detectors and two proprietary...
In this work we present three methods to improve a deep convolutional neural network approach to near-infrared heterogeneous face recognition. We first present a method to distill extra information from a pre-trained visible face network through the output logits of the network. Next, we put forth an altered contrastive loss function that uses the ℓ1 norm instead of the ℓ2 norm as a distance metric...
We propose the Component Bio-Inspired Feature (CBIF) with a moving segmentation scheme for age estimation. The CBIF defines a superset for the commonly used Bio-Inspired Feature (BIF) with more parameters and flexibility in settings, resulting in features with abundant characteristics. An in-depth study is performed for the determination of the parameters good for capturing age-related traits. The...
Recently, capabilities of many computer vision tasks have significantly improved due to advances in Convolutional Neural Networks. In our research, we demonstrate that it can be also used for face detection from low resolution thermal images, acquired with a portable camera. The physical size of the camera used in our research allows for embedding it in a wearable device or indoor remote monitoring...
This paper develops a status-aware projection metric learning (SPML) method for facial image-based kinship verification, especially for the parent-child kinship. Kinship verification for parent-child is considered to be an asymmetrical metric process, in that parents and children are associated with different status where the parents are priorly known to be significantly older than the children. Accordingly,...
Selfies have become commonplace. More and more people take pictures of themselves, and enjoy enhancing these pictures using a variety of image processing techniques. One specific functionality of interest is automatic skin and hair segmentation, as this allows for processing one's skin and hair separately. Traditional approaches require user input in the form of fully specified trimaps, or at least...
Face hallucination, which refers to predicting a HighResolution (HR) face image from an observed Low-Resolution (LR) one, is a challenging problem. Most state-of-the-arts employ local face structure prior to estimate the optimal representations for each patch by the training patches of the same position, and achieve good reconstruction performance. However, they do not take into account the contextual...
With the increasing number of public available training data for face alignment, the regression-based methods attracted much attention and have become the dominant methods to solve this problem. There are two main factors, the variance of the regression target and the capacity of the regression model, affecting the performance of the regression task. In this paper, we present a Stacked Hourglass Network...
Convolutional neural networks have significantly boosted the performance of face recognition in recent years due to its high capacity in learning discriminative features. In order to enhance the discriminative power of the deeply learned features, we propose a new supervision signal named marginal loss for deep face recognition. Specifically, the marginal loss simultaneously minimises the intra-class...
In this paper, a face recognition method based on Convolution Neural Network (CNN) is presented. This network consists of three convolution layers, two pooling layers, two full-connected layers and one Softmax regression layer. Stochastic gradient descent algorithm is used to train the feature extractor and the classifier, which can extract the facial features and classify them automatically. The...
Face recognition methods utilizing Sparse Representation based Classification (SRC) and Collaborative Representation based Classification (CRC) have recently attracted a great deal of attention due to inherent simplicity and efficiency. In this paper, we introduce the Large Margin Nearest Neighbor (LMNN), which learns a Mahalanobis distance metric that is applied, to SRC and CRC as the locality constraint...
Face editing has a variety of applications, especially with the increasing popularity of photography using mobile devices. In this work, we argue that the performance of face image editing can be further improved by using semantic segmentation which marks each pixel with a label that indicates its corresponding facial part. To this end, we propose a deep learning based method for automatic pixel-level...
In this paper we proposed a 4-stage coarse-to-fine framework to tackle the facial landmark localization problem in-the-wild. In our system, we first predict the landmark key points on a coarse level of granularity, which sets a good initialization for the whole framework. Then we group the key points into several components and refine each component with local patches cropped within them. After that...
In this paper, we present a new benchmark (Menpo benchmark) for facial landmark localisation and summarise the results of the recent competition, so-called Menpo Challenge, run in conjunction to CVPR 2017. The Menpo benchmark, contrary to the previous benchmarks such as 300-W and 300-VW, contains facial images both in (nearly) frontal, as well as in profile pose (annotated with a different markup...
This paper addresses the problem of automatically inferring personality traits of people talking to a camera. As in many other computer vision problems, Convolutional Neural Networks (CNN) models have shown impressive results. However, despite of the success in terms of performance, it is unknown what internal representation emerges in the CNN. This paper presents a deep study on understanding why...
Much research has been conducted on both face identification and face verification, with greater focus on the latter. Research on face identification has mostly focused on using closed-set protocols, which assume that all probe images used in evaluation contain identities of subjects that are enrolled in the gallery. Real systems, however, where only a fraction of probe sample identities are enrolled...
Deep learning based approaches proved to be dramatically effective to address many computer vision applications, including "face recognition in the wild". It has been extensively demonstrated that methods exploiting Deep Convolutional Neural Networks (DCNN) are powerful enough to overcome to a great extent many problems that negatively affected computer vision algorithms based on hand-crafted...
Heterogeneous face recognition (HFR) has a prominent importance in sophisticated face recognition systems. Thermal to visible scenario, where the gallery and the probe images are respectively captured in visible and long wavelength infrared (LWIR) band, is one of the most challenging and interesting HFR scenarios. Since the formation of thermal images does not require an external illumination source,...
Deep learning is used for all kinds of tasks which require human-like performance, such as voice and image recognition in smartphones, smart home technology, and self-driving cars. While great advances have been made in the field, results are often not satisfactory when compared to human performance. In the field of facial emotion recognition, especially in the wild, Convolutional Neural Networks...
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