Eigenface

About: Eigenface is a research topic. Over the lifetime, 2128 publications have been published within this topic receiving 110119 citations.

An efficient face recognition algorithm based on robust principal component analysis

Abstract: In this paper, an efficient face recognition algorithm is proposed, which is robust to illumination, expression and occlusion. In our method, a human face image is considered as a multiplication of a reflectance image and an illumination image. Then, this illumination model is used to transfer input images. After the transformation, the robust principal component analysis is employed to recover the intrinsic information of a sequence of images of one person. Finally, a new similarity metric is defined for face recognition. Experiments based on different databases illustrate that our method can achieve consistent and promising results.

Discriminant-component eigenfaces for privacy-preserving face recognition

Abstract: Over the past decades, face recognition has been a problem of critical interest in the machine learning and signal processing communities. However, conventional approaches such as eigenfaces do not protect the privacy of user data, which is emerging as an important design consideration in today's society. In this work, we leverage a supervised-learning subspace projection method called Discriminant Component Analysis (DCA) for privacy-preserving face recognition. By projecting the data onto the lower-dimensional signal subspace prescribed by DCA, high performance of face recognition is achievable without compromising privacy of the data owners. We evaluate our approach on three image datasets: Yale, Olivetti and Glasses datasets — the last is derived from the former two. Our approach can serve as a key enabler for real-world deployment of privacy-preserving face recognition applications, and provides a promising direction to researchers and private sectors.

Where is my puppy? Retrieving lost dogs by facial features

Abstract: A pet that goes missing is among many people's worst fears: a moment of distraction is enough for a dog or a cat wandering off from home. Some measures help matching lost animals to their owners; but automated visual recognition is one that -- although convenient, highly available, and low-cost -- is surprisingly overlooked. In this paper, we inaugurate that promising avenue by pursuing face recognition for dogs. We contrast four ready-to-use human facial recognizers (EigenFaces, FisherFaces, LBPH, and a Sparse method) to two original solutions based upon convolutional neural networks: BARK (inspired in architecture-optimized networks employed for human facial recognition) and WOOF (based upon off-the-shelf OverFeat features). Human facial recognizers perform poorly for dogs (up to 60.5 % accuracy), showing that dog facial recognition is not a trivial extension of human facial recognition. The convolutional network solutions work much better, with BARK attaining up to 81.1 % accuracy, and WOOF, 89.4 %. The tests were conducted in two datasets: Flickr-dog, with 42 dogs of two breeds (pugs and huskies); and Snoopybook, with 18 mongrel dogs.

Nonlinear face recognition based on maximum average margin criterion

Abstract: This paper proposes a novel nonlinear discriminant analysis method named by kernerlized maximum average margin criterion (KMAMC), which has combined the idea of support vector machine with the kernel fisher discriminant analysis (KFD). We also use a simple method to prove the relationship between both kernel methods. The difference of KMAMC from traditional KFD methods include: (1) the within-class and between-class scatter matrices are computed based on the support vectors instead of all the samples; (2) multiple centers are exploited instead of the single center in computing the two scatter matrices; (3) the discriminant criteria is formulated as subtracting the trace of within-class scatter matrix from that of the between-class scatter matrix, therefore, the tedious singularity problem is avoided. These features have made KMAMC more practical for real-world applications. Our experiments on two face databases, the FERET and CAS-PEAL face database, have illustrated its excellent performance compared with some traditional methods such as Eigenface, Fisherface, and KFD.

Kalman Filtering for Pose-Invariant Face Recognition

Abstract: We propose a novel algorithm for the identification of faces from image samples. The algorithm uses the Kalman filter to identify significant facial traits. Kalmanfaces are compact visual models that represent the invariant proportions of face classes. We employ the Kalmanfaces approach on the UMIST database, a collection of face images that were recorded under varying camera angles. Kalmanfaces show robustness against invisible facial traits and outperform the classic eigenfaces approach in terms of identification performance and algorithm speed. The paper discusses Kalmanfaces extraction, application, tunable parameters, experimental results and related work on Kalman filter application in face recognition.