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).
The last groups of techniques, so-called hybrid techniques, combine elements from other categories or use multiple representations to increase the ear recognition performance @cite_12 . Techniques from this group offer superior performance compared to competing techniques, but often at the cost of higher computational complexity @cite_15 @cite_0 @cite_13 . As suggested by recent surveys on ear recognition @cite_12 @cite_17 @cite_32 , hybrid techniques together with local descriptor-based methods represent the current state-of-the-art in this area.
@cite_32 @cite_0 @cite_15 @cite_13 @cite_12 @cite_17
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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. Ear recognition is a new biometrics technique. Due to its unique physiological structure, position and stability, ear recognition is expected to be a promising authentication technique. In this paper, a hybrid system for classifying ear images is proposed. This system combines independent component analysis (ICA) and RBF network. The original ear image database is decomposed into linear combinations of several basic images. Then the corresponding coefficients of these combinations are fed up into RBF network instead of an original feature vector comprised of pixel values of grayscale images. The local features extraction of ICA and the adaptability of RBF neural network are combined reasonably. The robustness of the system is enhanced. The experiment results show that the recognition rate of ICA RBF method is improved substantially. The use of multiple distant microphones has been widely studied for meeting recognition. The two most widely used approaches are 1) combination at the signal level, via blind beamforming, followed by recognition of a single enhanced audio signal, and 2) independent, logically parallel recognition of the multiple audio sources followed by hypothesis-level combination. In this paper we investigate how these two approaches compare for state-of-the-art recognition systems applied to meeting data from the two most recent NIST Rich Transcription evaluations. Our results show that beamforming is the superior approach, giving more accurate results while being inherently less computationally demanding. We then propose a hybrid approach that leverages both beamforming and signal-level diversity for systemcombination, and showthat this approach gives gains over either of the old methods. The amount of music information available on the Web is rapidly increasing. There is a pressing need for music information extraction. To extract useful information from natural language text, we must recognize music named entities first. This paper introduces a hybrid method to identify the Chinese named entities in music domain. Recently, machine learning approaches are frequently used to solve Name Entity Recognition (NER). So our method uses a Hidden Markov Model (HMM) as the underlying method. Since HMM has innate weaknesses, we incorporate it with rule-based method for pre-processing and post-processing. The combination of machine learning method and rule-based method results in a high precision recognition. And we improve both training and recognizing process of HMM for Music Named Entity Recognition (MNER). In this paper, a novel and convenient Musical Name Entity (MNE) tagging method to generate training data is proposed, which makes HMM method practically usable. In addition, we present an effective method of unknown words tagging in recognition. The experimental results show that our framework brings significant improvements for solving MNER.