Research On Some Key Issues In Face Recognition Under Complex Conditions

As the coming of the 21st century,face recognition is facing an important and difficult period.During the passed 40 years,a lot of theories and successful algorithms have been studied and proposed,and most key issues of face recognition under controlled conditions have been solved.While face recognition under nonideal condition, uncooperative condition,or large scale face databases,is still an unsolved problem.Till now,many algorithms were proposed for face recognition under complex conditions, but the research on this topic is just the beginning and all the new algorithms are at the exploratory stage.In this paper,we give deep analysis on face recognition under complex conditions. We specially focus on solving the effect from lighting and occlusion,and extracting the robust & adaptive face features.We proposed several new models and algorithms.The novel models and algorithms proposed in this paper are illustrated as follows:1.An illumination invariant face reconstructing model is proposed.Based on the Total Variation theory,this model extracts illumination invariant face features from both large and small scale information;also,it preserves all the key features.In the experimental parts,we give qualitative analysis about this reconstructing model and using image entropy as the quantitative analysis.Compared with conventional histogram equalization algorithm and the newly proposed quotient image algorithm,our model outperforms them based on both the visual sense and the image entropy.To further prove the validity of our model,we use some famous subspace analysis algorithms to extract the features of face images reconstructed by the proposed model.The experimental results prove the reconstructing model could largely improve the face recognition rates.2.A supervised occlusion invariant face reconstructing model is proposed.The proposed model is composed by two important parts:occlusion invariant reconstructing coefficients extracting and adaptive supervised mask of occlusion generating. From the visual evaluation point,the proposed model could effectively remove the occlusion in the original face samples,and then generate an ideal occlusion invariant face image.Since the model would induce noise,we further propose an adaptive face reconstructing model which could preserve the facial features while removing the noisy information.Based on the qualitative analysis,the recon- structed face images have good visual perceptions and they are close to the true face samples collected under normal conditions.Based on the quantitative analysis, the Euclidean distance between the reconstructed face samples of the same person is obviously reduced.Therefore,the lace samples are the normalized result and they could be used as the occlusion invariant samples for the subsequent recognition algorithms.3.Testing and Evaluation of the robust & adaptive property of current standard subspace analysis algorithms.The conception of robust & adaptive subspace analvsis algorithm is firstly proposed.We give deep analysis about the robust & adaptive property(definition referred to section 3 in chapter 4) of current standard subspace analysis algorithms.In the experiments,we propose two new testing methods for face recognition:the narrow-sense open set testing and the narrow-sense close set testing(definition referred to section 3 in chapter 4).The most popular four face databases are adopted to evaluate the recognition performance.Based on the summary,analysis and experimental results in this paper,we conclude the following conceptions which would provide reference significance for further research on subspace analysis algorithms.1).The unsupervised algorithms have better robust & adaptive property compared with the supervised algorithms.Only when no individual not belong to the training set needs to be recognized or the training set are representative,the supervised algorithms outperform the unsupervised ones.2).Among all the unsupervised algorithms,the independent component analysis algorithm has the best robust & adaptive property.It achieves the best performance in both the narrow-sense close set testing and the narrow-sense open set testing.3).The face recognition algorithms based on the independent component analysis are the typical robust & adaptive subspace analysis algorithms,and the ICA algorithm could be used as the reference algorithm.4.Non-linear intrinsic codes extraction model and robust & adaptive face recognition algorithm.Up to now,no algorithm could resolve all the problems of illumination effect,expression,occlusion,and low recognition rate etc.at the same time. Based on the research work in the first three sections,we propose a novel systematic robust & adaptive face recognition algorithm which could solve most existing problems.In the algorithm,we propose an Expressive Feature Analysis(EFA) model and a Non-linear Intrinsic Codes Extraction(NICE) model.The EFA model is mainly used to replace the conventional PCA model to reduce the dimensions of the original face samples.It could enhance the effective information represented by the scale of variables in statistics while reducing the dimensions.Therefore,it guarantees that the most discriminate features would be extracted in the following steps.The NICE model is used as the coding part of the input face samples.The intrinsic codes extracted by it have good robust & adaptive property;also,they have good discriminant property.These intrinsic codes used for face recognition could achieve good performance.We execute both narrow-sense open set testing and close set testing experiments on the famous CAS-PEAL,FERET,Yale-B and CMU PIE face databases.The experimental results further prove the robust & adaptive property of our algorithm.The recognition rates of our algorithm are higher than that of the ICA algorithm employing Gabor analysis.