Context-Patch Face Hallucination Based on Thresholding Locality-Constrained Representation and Reproducing Learning

Face hallucination is a technique that reconstruct high-resolution (HR) faces from low-resolution (LR) faces, by using the prior knowledge learned from HR/LR face pairs. Most state-of-the-arts leverage position-patch prior knowledge of human face to estimate the optimal representation coefficients for each image patch. However, they focus only the position information and usually ignore the context information of image patch. In addition, when they are confronted with misalignment or the Small Sample Size (SSS) problem, the hallucination performance is very poor. To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL). Under the context-patch based framework, we advance a thresholding based representation method to enhance the reconstruction accuracy and reduce the computational complexity. To further improve the performance of the proposed algorithm, we propose a promotion strategy called reproducing learning. By adding the estimated HR face to the training set, which can simulates the case that the HR version of the input LR face is present in the training set, thus iteratively enhancing the final hallucination result. Experiments demonstrate that the proposed TLcR-RL method achieves a substantial increase in the hallucinated results, both subjectively and objectively. Additionally, the proposed framework is more robust to face misalignment and the SSS problem, and its hallucinated HR face is still very good when the LR test face is from the real-world. The MATLAB source code is available at https://github.com/junjun-jiang/TLcR-RL

A PSYCHOLINGUISTIC ANALYSIS OF SCHIZOPHRENIC SPEECH REFLECTING HALLUCINATION AND DELUSION IN THE CAVEMAN’S VALENTINE

The objectives of this research are (1) to explain the speech abnormalities of a schizophrenic character, Romulus, in The Caveman’s Valentine; and (2) to present the characteristics of schizophrenia represented by Romulus in his speech. This research employed a descriptive qualitative method. It was concerned with the description of the data in the form of utterances produced by the schizophrenic character, Romulus, in which the phenomena of schizophrenic speech abnormalities exist. Quantification of the data was also done in this research, only to strengthen the answer of the first objective. Meanwhile, for the second objective, the explanation is without number. Finally, in order to support the credibility of the data findings, data trustworthiness was maintained in the form of triangulation and peer discussion (peer debriefing). The findings of this research show that first, among the eight types of schizophrenic speech abnormalities, only four of them occur. They are looseness, perseveration of ideas, peculiar use of words, and non-logical reasoning (peculiar logic). Looseness is the first most-often appearing phenomenon, followed by perseveration of ideas, peculiar use of words, and non-logical reasoning (peculiar logic). Second, all characteristics of schizophrenia, i.e. hallucination and delusion, are also shown in the movie. Hallucination is represented by the occurrence of visual and auditory hallucination, while delusion is represented by the occurrence of paranoid delusion and delusion of reference. In addition, for the characteristics of schizophrenia, the number of the occurrence of each phenomenon is not important since the existence of each characteristic is enough to judge that someone suffers from schizophrenia. Keywords : schizophrenia, looseness, perseveration of ideas, peculiar use of words, non-logical reasoning (peculiar logic), hallucination, delusion, The Caveman’s Valentin

Learning to Hallucinate Face Images via Component Generation and Enhancement

We propose a two-stage method for face hallucination. First, we generate facial components of the input image using CNNs. These components represent the basic facial structures. Second, we synthesize fine-grained facial structures from high resolution training images. The details of these structures are transferred into facial components for enhancement. Therefore, we generate facial components to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage. The experiments demonstrate that our method performs favorably against state-of-the-art methodsComment: IJCAI 2017. Project page: http://www.cs.cityu.edu.hk/~yibisong/ijcai17_sr/index.htm