Abstract
Recent years have witnessed the rapid development of face image hallucination techniques. However, the previous face hallucination methods are unsupervised and ignore the label information of training samples, leading to undesirable results. This article proposes a locality-constrained and category embedding representation (LCER) method to super-resolve face image in a supervised manner by embedding the label information in data representation. The proposed LCER incorporates the locality prior and category information into one unified framework, which aims to learn both the advantages of locality in preserving the true typologic structure of data manifold and the discriminability in exposing the class subspace information. Such strategy allows the LCER not only to preserve more sharpen image details but also to guarantee the face structure pattern be transferred mainly from the same subject in super-resolution reconstruction. Extensive experiments were conducted to evaluate the proposed LCER, and the comparative results demonstrate that it achieved superior face hallucination performance in both the quantitative measurements and visual impressions compared to several state-of-the-art.
Highlights
F ACE hallucination, aiming to promote the performance of face recognition [1], is such a domain-specific superresolution technique that tries to infer high-resolution (HR) face images from the corresponding low-resolution (LR) observations
In this article, we propose a new discriminative face hallucination method named locality-constrained and category embedding representation (LCER) from a different point of view to previous ones
This article presented a novel LCER for discriminative face image super-resolution
Summary
F ACE hallucination, aiming to promote the performance of face recognition [1], is such a domain-specific superresolution technique that tries to infer high-resolution (HR) face images from the corresponding low-resolution (LR) observations. The aforementioned face hallucination methods mainly focus on the manifold structure assumption but rarely take into consideration of the discriminative information of training data, which significantly benefits the image representation and classification applications [42], [43] To address this concern, in this article, we propose a new discriminative face hallucination method named locality-constrained and category embedding representation (LCER) from a different point of view to previous ones. The advantages of using locality prior as well as label information admits the proposed LCER method to preserve more image details and desirable face patterns in super-resolution reconstruction. 2) The discriminative term that exploits the local information for super-resolution representation in LCER is interpreted from the Bayes probability perspective This ensures the rationality of the proposed model and demonstrates the capacity of LCER in preserving the face subspace features from the statistical viewpoint.
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