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Featured researches published by Jinli Suo.


IEEE Transactions on Pattern Analysis and Machine Intelligence | 2010

A Compositional and Dynamic Model for Face Aging

Jinli Suo; Song-Chun Zhu; Shiguang Shan; Xilin Chen

In this paper, we present a compositional and dynamic model for face aging. The compositional model represents faces in each age group by a hierarchical And-or graph, in which And nodes decompose a face into parts to describe details (e.g., hair, wrinkles, etc.) crucial for age perception and Or nodes represent large diversity of faces by alternative selections. Then a face instance is a transverse of the And-or graph-parse graph. Face aging is modeled as a Markov process on the parse graph representation. We learn the parameters of the dynamic model from a large annotated face data set and the stochasticity of face aging is modeled in the dynamics explicitly. Based on this model, we propose a face aging simulation and prediction algorithm. Inversely, an automatic age estimation algorithm is also developed under this representation. We study two criteria to evaluate the aging results using human perception experiments: (1) the accuracy of simulation: whether the aged faces are perceived of the intended age group, and (2) preservation of identity: whether the aged faces are perceived as the same person. Quantitative statistical analysis validates the performance of our aging model and age estimation algorithm.


computer vision and pattern recognition | 2007

A Multi-Resolution Dynamic Model for Face Aging Simulation

Jinli Suo; Feng Min; Song-Chun Zhu; Shiguang Shan; Xilin Chen

In this paper we present a dynamic model for simulating face aging process. We adopt a high resolution grammatical face model [1] and augment it with age and hair features. This model represents all face images by a multi-layer and-or graph and integrates three most prominent aspects related to aging changes: global appearance changes in hair style and shape, deformations and aging effects of facial components, and wrinkles appearance at various facial zones. Then face aging is modeled as a dynamic Markov process on this graph representation which is learned from a large dataset. Given an input image, we firstly compute the graph representation, and then sample the graph structures over various age groups according to the learned dynamic model. Finally we generate new face images with the sampled graphs. Our approach has three novel aspects: (1) the aging model is learned from a dataset of 50,000 adult faces at different ages; (2) we explicitly model the uncertainty in face aging andean sample multiple plausible aged faces for an input image; and (3) we conduct a simple human experiment to validate the simulated aging process.


ieee international conference on automatic face & gesture recognition | 2008

Design sparse features for age estimation using hierarchical face model

Jinli Suo; Tianfu Wu; Song-Chun Zhu; Shiguang Shan; Xilin Chen; Wen Gao

A key point in automatic age estimation is to design feature set essential to age perception. To achieve this goal, this paper builds up a hierarchical graphical face model for faces appearing at low, middle and high resolution respectively. Along the hierarchy, a face image is decomposed into detailed parts from coarse to fine. Then four types of features are extracted from this graph representation guided by the priors of aging process embedded in the graphical model: topology, geometry, photometry and configuration. On age estimation, this paper follows the popular regression formulation for mapping feature vectors to its age label. The effectiveness of the presented feature set is justified by testing results on two datasets using different kinds of regression methods. The experimental results in this paper show that designing feature set for age estimation under the guidance of hierarchical face model is a promising method and a flexible framework as well.


IEEE Transactions on Pattern Analysis and Machine Intelligence | 2012

A Concatenational Graph Evolution Aging Model

Jinli Suo; Xilin Chen; Shiguang Shan; Wen Gao; Qionghai Dai

Modeling the long-term face aging process is of great importance for face recognition and animation, but there is a lack of sufficient long-term face aging sequences for model learning. To address this problem, we propose a CONcatenational GRaph Evolution (CONGRE) aging model, which adopts decomposition strategy in both spatial and temporal aspects to learn long-term aging patterns from partially dense aging databases. In spatial aspect, we build a graphical face representation, in which a human face is decomposed into mutually interrelated subregions under anatomical guidance. In temporal aspect, the long-term evolution of the above graphical representation is then modeled by connecting sequential short-term patterns following the Markov property of aging process under smoothness constraints between neighboring short-term patterns and consistency constraints among subregions. The proposed model also considers the diversity of face aging by proposing probabilistic concatenation strategy between short-term patterns and applying scholastic sampling in aging prediction. In experiments, the aging prediction results generated by the learned aging models are evaluated both subjectively and objectively to validate the proposed model.


international conference on computer vision | 2009

Learning long term face aging patterns from partially dense aging databases

Jinli Suo; Xilin Chen; Shiguang Shan; Wen Gao

Studies on face aging are handicapped by lack of long term dense aging sequences for model training. To handle this problem, we propose a new face aging model, which learns long term face aging patterns from partially dense aging databases. The learning strategy is based on two assumptions: (i) short term face aging pattern is relatively simple and is possible to be learned from currently available databases; (ii) long term face aging is a continuous and smooth Markov process. Adopting a compositional face representation, our aging algorithm learns a function-based short term aging model from real aging sequences to infer facial parameters within a short age span. Based on the predefined smoothness criteria between two overlapping short term aging patterns, we concatenate these learned short term aging patterns to build the long term aging patterns. Both the subjective assessment and objective evaluations of synthetic aging sequences validate the effectiveness of the proposed model.


Optics Letters | 2014

Content adaptive illumination for Fourier ptychography

Liheng Bian; Jinli Suo; Guohai Situ; Guoan Zheng; Feng Chen; Qionghai Dai

Fourier ptychography (FP) is a recently reported technique, for large field-of-view and high-resolution imaging. Specifically, FP captures a set of low-resolution images, under angularly varying illuminations, and stitches them together in the Fourier domain. One of FPs main disadvantages is its long capturing process, due to the requisite large number of incident illumination angles. In this Letter, utilizing the sparsity of natural images in the Fourier domain, we propose a highly efficient method, termed adaptive Fourier ptychography (AFP), which applies content adaptive illumination for FP, to capture the most informative parts of the scenes spatial spectrum. We validate the effectiveness and efficiency of the reported framework, with both simulated and real experiments. Results show that the proposed AFP could shorten the acquisition time of conventional FP, by around 30%-60%.


Optics Express | 2015

Fourier ptychographic reconstruction using Wirtinger flow optimization

Liheng Bian; Jinli Suo; Guoan Zheng; Kaikai Guo; Feng Chen; Qionghai Dai

Recently Fourier Ptychography (FP) has attracted great attention, due to its marked effectiveness in leveraging snapshot numbers for spatial resolution in large field-of-view imaging. To acquire high signal-to-noise-ratio (SNR) images under angularly varying illuminations for subsequent reconstruction, FP requires long exposure time, which largely limits its practical applications. In this paper, based on the recently reported Wirtinger flow algorithm, we propose an iterative optimization framework incorporating phase retrieval and noise relaxation together, to realize FP reconstruction using low SNR images captured under short exposure time. Experiments on both synthetic and real captured data validate the effectiveness of the proposed reconstruction method. Specifically, the proposed technique could save ~ 80% exposure time to achieve similar retrieval accuracy compared to the conventional FP. Besides, we have released our source code for non-commercial use.


IEEE Transactions on Systems, Man, and Cybernetics | 2014

Reweighted low-rank matrix recovery and its application in image restoration.

Yigang Peng; Jinli Suo; Qionghai Dai; Wenli Xu

In this paper, we propose a reweighted low-rank matrix recovery method and demonstrate its application for robust image restoration. In the literature, principal component pursuit solves low-rank matrix recovery problem via a convex program of mixed nuclear norm and ℓ1 norm. Inspired by reweighted ℓ1 minimization for sparsity enhancement, we propose reweighting singular values to enhance low rank of a matrix. An efficient iterative reweighting scheme is proposed for enhancing low rank and sparsity simultaneously and the performance of low-rank matrix recovery is prompted greatly. We demonstrate the utility of the proposed method both on numerical simulations and real images/videos restoration, including single image restoration, hyperspectral image restoration, and background modeling from corrupted observations. All of these experiments give empirical evidence on significant improvements of the proposed algorithm over previous work on low-rank matrix recovery.


international conference on computational photography | 2013

Coded focal stack photography

Xing Lin; Jinli Suo; Gordon Wetzstein; Qionghai Dai; Ramesh Raskar

We present coded focal stack photography as a computational photography paradigm that combines a focal sweep and a coded sensor readout with novel computational algorithms. We demonstrate various applications of coded focal stacks, including photography with programmable non-planar focal surfaces and multiplexed focal stack acquisition. By leveraging sparse coding techniques, coded focal stacks can also be used to recover a full-resolution depth and all-in-focus (AIF) image from a single photograph. Coded focal stack photography is a significant step towards a computational camera architecture that facilitates high-resolution post-capture refocusing, flexible depth of field, and 3D imaging.


computer vision and pattern recognition | 2014

Transparent Object Reconstruction via Coded Transport of Intensity

Chenguang Ma; Xing Lin; Jinli Suo; Qionghai Dai; Gordon Wetzstein

Capturing and understanding visual signals is one of the core interests of computer vision. Much progress has been made w.r.t. many aspects of imaging, but the reconstruction of refractive phenomena, such as turbulence, gas and heat flows, liquids, or transparent solids, has remained a challenging problem. In this paper, we derive an intuitive formulation of light transport in refractive media using light fields and the transport of intensity equation. We show how coded illumination in combination with pairs of recorded images allow for robust computational reconstruction of dynamic two and three-dimensional refractive phenomena.

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Guoan Zheng

University of Connecticut

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Shiguang Shan

Chinese Academy of Sciences

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