Fisher Vector Faces in the Wild.
Reads0
Chats0
TLDR
This paper shows that Fisher vectors on densely sampled SIFT features are capable of achieving state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” benchmark, and shows that a compact descriptor can be learnt from them using discriminative metric learning.Abstract:
Several recent papers on automatic face verification have significantly raised the performance bar by developing novel, specialised representations that outperform standard features such as SIFT for this problem. This paper makes two contributions: first, and somewhat surprisingly, we show that Fisher vectors on densely sampled SIFT features, i.e. an off-the-shelf object recognition representation, are capable of achieving state-of-the-art face verification performance on the challenging “Labeled Faces in the Wild” benchmark; second, since Fisher vectors are very high dimensional, we show that a compact descriptor can be learnt from them using discriminative metric learning. This compact descriptor has a better recognition accuracy and is very well suited to large scale identification tasks.read more
Citations
More filters
Proceedings ArticleDOI
DeepFace: Closing the Gap to Human-Level Performance in Face Verification
TL;DR: This work revisits both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network.
Proceedings ArticleDOI
Deep face recognition
TL;DR: It is shown how a very large scale dataset can be assembled by a combination of automation and human in the loop, and the trade off between data purity and time is discussed.
Proceedings ArticleDOI
Multi-view Convolutional Neural Networks for 3D Shape Recognition
TL;DR: In this article, a CNN architecture is proposed to combine information from multiple views of a 3D shape into a single and compact shape descriptor, which can be applied to accurately recognize human hand-drawn sketches of shapes.
Proceedings ArticleDOI
Deep Learning Face Representation from Predicting 10,000 Classes
Yi Sun,Xiaogang Wang,Xiaoou Tang +2 more
TL;DR: It is argued that DeepID can be effectively learned through challenging multi-class face identification tasks, whilst they can be generalized to other tasks (such as verification) and new identities unseen in the training set.
Posted Content
Multi-view Convolutional Neural Networks for 3D Shape Recognition
TL;DR: This work presents a standard CNN architecture trained to recognize the shapes' rendered views independently of each other, and shows that a 3D shape can be recognized even from a single view at an accuracy far higher than using state-of-the-art3D shape descriptors.
References
More filters
Journal ArticleDOI
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Journal ArticleDOI
Eigenfaces vs. Fisherfaces: recognition using class specific linear projection
TL;DR: A face recognition algorithm which is insensitive to large variation in lighting direction and facial expression is developed, based on Fisher's linear discriminant and produces well separated classes in a low-dimensional subspace, even under severe variations in lighting and facial expressions.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Proceedings ArticleDOI
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
TL;DR: This paper presents a method for recognizing scene categories based on approximate global geometric correspondence that exceeds the state of the art on the Caltech-101 database and achieves high accuracy on a large database of fifteen natural scene categories.