DeepFace 2: Face Alignment with Conditional Random Field


DeepFace 2: Face Alignment with Conditional Random Field – We present a novel framework to solve a large-scale face alignment problem. Our scheme consists of three phases: (1) face alignment through constraint set on the constraint, (2) face alignment through prediction on face model, and (3) face alignment through face transformation. All of our proposed framework operates on a constrained face model, and is very fast and scalable to support large-scale face alignment. We evaluated our framework on the MNIST face alignment dataset provided by Google Scholar, where it achieves competitive performance compared to a state-of-the-art bounding box fusion method and a state-of-the-art 3D face alignment method.

This paper addresses the problem of semantic segmentation of faces from videos. We show that for the majority of videos without missing edges, the resulting segmenting task can be efficiently computed by a deep learning approach. A key limitation of deep learning methods is data-constrained nature of video data, which significantly limits the effectiveness of deep learning. To address this challenge, we propose to use a large, noisy, and often noisy, video set as a model to extract the semantic segmentation information. To address this problem, we propose a fast and scalable method for multi-scale segmentation with a dataset of more than 16M frames. The proposed method is based on the first step of the convolutional neural network (CNN) model which models the face and the image as input and has a multi-layer layer architecture which is learned by leveraging the convolutional layers. The performance of the CNN model is evaluated using extensive experiments on four different benchmark datasets. We have evaluated our approach using a challenging benchmark, COCO-200, and achieved the best performance when using only 10% of the entire test set.

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DeepFace 2: Face Alignment with Conditional Random Field

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  • Affective surveillance systems: An affective feature approach

    Spatio-Temporal Feature Learning for Robust Face RecognitionThis paper addresses the problem of semantic segmentation of faces from videos. We show that for the majority of videos without missing edges, the resulting segmenting task can be efficiently computed by a deep learning approach. A key limitation of deep learning methods is data-constrained nature of video data, which significantly limits the effectiveness of deep learning. To address this challenge, we propose to use a large, noisy, and often noisy, video set as a model to extract the semantic segmentation information. To address this problem, we propose a fast and scalable method for multi-scale segmentation with a dataset of more than 16M frames. The proposed method is based on the first step of the convolutional neural network (CNN) model which models the face and the image as input and has a multi-layer layer architecture which is learned by leveraging the convolutional layers. The performance of the CNN model is evaluated using extensive experiments on four different benchmark datasets. We have evaluated our approach using a challenging benchmark, COCO-200, and achieved the best performance when using only 10% of the entire test set.


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