Identity recognition from ear images is an active field of research within the biometric community. The ability to capture ear images from a distance and in a covert manner makes ear recognition technology an appealing choice for surveillance and security applications as well as related application domains. In contrast to other biometric modalities, where large datasets captured in uncontrolled settings are readily available, datasets of ear images are still limited in size and mostly of laboratory-like quality. As a consequence, ear recognition technology has not benefited yet from advances in deep learning and convolutional neural networks (CNNs) and is still lacking behind other modalities that experienced significant performance gains owing to deep recognition technology. In this paper we address this problem and aim at building a CNNbased ear recognition model. We explore different strategies towards model training with limited amounts of training data and show that by selecting an appropriate model architecture, using aggressive data augmentation and selective learning on existing (pre-trained) models, we are able to learn an effective CNN-based model using a little more than 1300 training images. The result of our work is the first CNN-based approach to ear recognition that is also made publicly available to the research community. With our model we are able to improve on the rank one recognition rate of the previous state-of-the-art by more than 25 on a challenging dataset of ear images captured from the web (a.k.a. in the wild).
Holistic techniques rely on the global ear appearance and exploit representations that encode the ear structure as whole. As the appearance of ears varies significantly with pose or illumination, care needs to be taken before computing holistic features from input images and normalization techniques need to be applied to correct for these changes prior to feature extraction. Examples of global techniques can be found in @cite_9 @cite_39 @cite_37 .
@cite_9 @cite_37 @cite_39
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In the literature of psychophysics and neurophysiology, many studies have shown that both global and local features are crucial for face representation and recognition. This paper proposes a novel face recognition method which exploits both global and local discriminative features. In this method, global features are extracted from the whole face images by keeping the low-frequency coefficients of Fourier transform, which we believe encodes the holistic facial information, such as facial contour. For local feature extraction, Gabor wavelets are exploited considering their biological relevance. After that, Fisher's linear discriminant (FLD) is separately applied to the global Fourier features and each local patch of Gabor features. Thus, multiple FLD classifiers are obtained, each embodying different facial evidences for face recognition. Finally, all these classifiers are combined to form a hierarchical ensemble classifier. We evaluate the proposed method using two large-scale face databases: FERET and FRGC version 2.0. Experiments show that the results of our method are impressively better than the best known results with the same evaluation protocol. If fusion rules cannot adapt to the changes of environment and individual users, multimodal systems may perform worse than unimodal systems when one or more modalities encounter data degeneration. This paper develops a robust face and ear based multimodal biometric system using Sparse Representation (SR), which integrates the face and ear at feature level, and can effectively adjust the fusion rule based on reliability difference between the modalities. We first propose a novel index called Sparse Coding Error Ratio (SCER) to measure the reliability difference between face and ear query samples. Then, SCER is utilized to develop an adaptive feature weighting scheme for dynamically reducing the negative effect of the less reliable modality. In multimodal classification phase, SR-based classification techniques are employed, i.e., Sparse Representation based Classification (SRC) and Robust Sparse Coding (RSC). Finally, we derive a category of SR-based multimodal recognition methods, including Multimodal SRC with feature Weighting (MSRCW) and Multimodal RSC with feature Weighting (MRSCW). Experimental results demonstrate that: (a) MSRCW and MRSCW perform significantly better than the unimodal recognition using either face or ear alone, as well as the known multimodal methods; (b) The effectiveness of adaptive feature weighting is verified. MSRCW and MRSCW are very robust to the image degeneration occurring to one of the modalities. Even when face (ear) query sample suffers from 100 random pixel corruption, they can still get the performance close to the ear (face) unimodal recognition; (c) By integrating the advantages of adaptive feature weighting and sparsity-constrained regression, MRSCW seems excellent in tackling the face and ear based multimodal recognition problem. In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually. We address the pose mismatch problem which can occur in face verification systems that have only a single (frontal) face image available for training. In the framework of a Bayesian classifier based on mixtures of gaussians, the problem is tackled through extending each frontal face model with artificially synthesized models for non-frontal views. The synthesis methods are based on several implementations of maximum likelihood linear regression (MLLR), as well as standard multi-variate linear regression (LinReg). All synthesis techniques rely on prior information and learn how face models for the frontal view are related to face models for non-frontal views. The synthesis and extension approach is evaluated by applying it to two face verification systems: a holistic system (based on PCA-derived features) and a local feature system (based on DCT-derived features). Experiments on the FERET database suggest that for the holistic system, the LinReg-based technique is more suited than the MLLR-based techniques; for the local feature system, the results show that synthesis via a new MLLR implementation obtains better performance than synthesis based on traditional MLLR. The results further suggest that extending frontal models considerably reduces errors. It is also shown that the local feature system is less affected by view changes than the holistic system; this can be attributed to the parts based representation of the face, and, due to the classifier based on mixtures of gaussians, the lack of constraints on spatial relations between the face parts, allowing for deformations and movements of face areas.