3 research outputs found
Cross-Age LFW: A Database for Studying Cross-Age Face Recognition in Unconstrained Environments
Labeled Faces in the Wild (LFW) database has been widely utilized as the
benchmark of unconstrained face verification and due to big data driven machine
learning methods, the performance on the database approaches nearly 100%.
However, we argue that this accuracy may be too optimistic because of some
limiting factors. Besides different poses, illuminations, occlusions and
expressions, cross-age face is another challenge in face recognition. Different
ages of the same person result in large intra-class variations and aging
process is unavoidable in real world face verification. However, LFW does not
pay much attention on it. Thereby we construct a Cross-Age LFW (CALFW) which
deliberately searches and selects 3,000 positive face pairs with age gaps to
add aging process intra-class variance. Negative pairs with same gender and
race are also selected to reduce the influence of attribute difference between
positive/negative pairs and achieve face verification instead of attributes
classification. We evaluate several metric learning and deep learning methods
on the new database. Compared to the accuracy on LFW, the accuracy drops about
10%-17% on CALFW.Comment: 10 pages, 9 figure
A Fast and Accurate Unconstrained Face Detector
We propose a method to address challenges in unconstrained face detection,
such as arbitrary pose variations and occlusions. First, a new image feature
called Normalized Pixel Difference (NPD) is proposed. NPD feature is computed
as the difference to sum ratio between two pixel values, inspired by the Weber
Fraction in experimental psychology. The new feature is scale invariant,
bounded, and is able to reconstruct the original image. Second, we propose a
deep quadratic tree to learn the optimal subset of NPD features and their
combinations, so that complex face manifolds can be partitioned by the learned
rules. This way, only a single soft-cascade classifier is needed to handle
unconstrained face detection. Furthermore, we show that the NPD features can be
efficiently obtained from a look up table, and the detection template can be
easily scaled, making the proposed face detector very fast. Experimental
results on three public face datasets (FDDB, GENKI, and CMU-MIT) show that the
proposed method achieves state-of-the-art performance in detecting
unconstrained faces with arbitrary pose variations and occlusions in cluttered
scenes.Comment: This paper has been accepted by TPAMI. The source code is available
on the project page
http://www.cbsr.ia.ac.cn/users/scliao/projects/npdface/index.htm
Scalable Kernel K-Means Clustering with Nystrom Approximation: Relative-Error Bounds
Kernel -means clustering can correctly identify and extract a far more
varied collection of cluster structures than the linear -means clustering
algorithm. However, kernel -means clustering is computationally expensive
when the non-linear feature map is high-dimensional and there are many input
points. Kernel approximation, e.g., the Nystr\"om method, has been applied in
previous works to approximately solve kernel learning problems when both of the
above conditions are present. This work analyzes the application of this
paradigm to kernel -means clustering, and shows that applying the linear
-means clustering algorithm to features
constructed using a so-called rank-restricted Nystr\"om approximation results
in cluster assignments that satisfy a approximation ratio in
terms of the kernel -means cost function, relative to the guarantee provided
by the same algorithm without the use of the Nystr\"om method. As part of the
analysis, this work establishes a novel relative-error trace
norm guarantee for low-rank approximation using the rank-restricted Nystr\"om
approximation. Empirical evaluations on the million instance MNIST8M
dataset demonstrate the scalability and usefulness of kernel -means
clustering with Nystr\"om approximation. This work argues that spectral
clustering using Nystr\"om approximation---a popular and computationally
efficient, but theoretically unsound approach to non-linear clustering---should
be replaced with the efficient and theoretically sound combination of kernel
-means clustering with Nystr\"om approximation. The superior performance of
the latter approach is empirically verified