— Face Recognition using Discrete Cosine Transform (DCT) for Local and Global Features involves r... more — Face Recognition using Discrete Cosine Transform (DCT) for Local and Global Features involves recognizing the corresponding face image from the database. The face image obtained from the user is cropped such that only the frontal face image is extracted, eliminating the background. The image is restricted to a size of 128 × 128 pixels. All images in the database are gray level images. DCT is applied to the entire image. This gives DCT coefficients, which are global features. Local features such as eyes, nose and mouth are also extracted and DCT is applied to these features. Depending upon the recognition rate obtained for each feature, they are given weightage and then combined. Both local and global features are used for comparison. By comparing the ranks for global and local features, the false acceptance rate for DCT can be minimized. Keywords- face recognition; biometrics; person identification; authentication; discrete cosine transform; DCT; global local features. I.
My dear fellow exporters, EEPC India will be making a series of presentation with the Ministry of... more My dear fellow exporters, EEPC India will be making a series of presentation with the Ministry of Commerce & Industry, Government of India. The 1st meeting will be the Transaction Cost Core Group which will be chaired by the Hon'ble Minister of State for Commerce & Industry, Shri Jyotiraditya Scindia. In this meeting, EEPC India will be representing with the
Causality knowledge is vital to building robust AI systems. Deep learning models often perform po... more Causality knowledge is vital to building robust AI systems. Deep learning models often perform poorly on tasks that require causal reasoning, which is often derived using some form of commonsense knowledge not immediately available in the input but implicitly inferred by humans. Prior work has unraveled spurious observational biases that models fall prey to in the absence of causality. While language representation models preserve contextual knowledge within learned embeddings, they do not factor in causal relationships during training. By blending causal relationships with the input features to an existing model that performs visual cognition tasks (such as scene understanding, video captioning, video questionanswering, etc.), better performance can be achieved owing to the insight causal relationships bring about. Recently, several models have been proposed that have tackled the task of mining causal data from either the visual or textual modality. However, there does not exist wi...
Recently, learning-based models have enhanced the performance of single-image super-resolution (S... more Recently, learning-based models have enhanced the performance of single-image super-resolution (SISR). However, applying SISR successively to each video frame leads to a lack of temporal coherency. Convolutional neural networks (CNNs) outperform traditional approaches in terms of image quality metrics such as peak signal to noise ratio (PSNR) and structural similarity (SSIM). On the other hand, generative adversarial networks (GANs) offer a competitive advantage by being able to mitigate the issue of a lack of finer texture details, usually seen with CNNs when super-resolving at large upscaling factors. We present iSeeBetter, a novel GAN-based spatio-temporal approach to video super-resolution (VSR) that renders temporally consistent super-resolution videos. iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Furthermore, to improve the “naturality” of the super-resolve...
In the domain of Biometrics, recognition systems based on iris, fingerprint or palm print scans e... more In the domain of Biometrics, recognition systems based on iris, fingerprint or palm print scans etc. are often considered more dependable due to extremely low variance in the properties of these entities with respect to time. However, over the last decade data processing capability of computers has increased manifold, which has made real-time video content analysis possible. This shows that
2010 11th International Conference on Control Automation Robotics & Vision, 2010
In this paper, an efficient method for face recognition based on the Discrete Cosine Transform (D... more In this paper, an efficient method for face recognition based on the Discrete Cosine Transform (DCT), Fisher Linear Discriminant (FLD) and classifier is presented. First, the dimensionality of the original face image is reduced using the DCT and illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. FLD is applied to the selected DCT coefficients to discriminate
Recognition systems are commonly designed to authenticate users at the access control levels of a... more Recognition systems are commonly designed to authenticate users at the access control levels of a system. A number of voice recognition methods have been developed using a pitch estimation process which are very vulnerable in low Signal to Noise Ratio (SNR) environments thus, these programs fail to provide the desired level of accuracy and robustness. Also, most text independent speaker recognition programs are incapable of coping with unauthorized attempts to gain access by tampering with the samples or reference database. The proposed text-independent voice recognition system makes use of multilevel cryptography to preserve data integrity while in transit or storage. Encryption and decryption follow a transform based approach layered with pseudorandom noise addition whereas for pitch detection, a modified version of the autocorrelation pitch extraction algorithm is used. The experimental results show that the proposed algorithm can decrypt the signal under test with exponentially ...
— Face Recognition using Discrete Cosine Transform (DCT) for Local and Global Features involves r... more — Face Recognition using Discrete Cosine Transform (DCT) for Local and Global Features involves recognizing the corresponding face image from the database. The face image obtained from the user is cropped such that only the frontal face image is extracted, eliminating the background. The image is restricted to a size of 128 × 128 pixels. All images in the database are gray level images. DCT is applied to the entire image. This gives DCT coefficients, which are global features. Local features such as eyes, nose and mouth are also extracted and DCT is applied to these features. Depending upon the recognition rate obtained for each feature, they are given weightage and then combined. Both local and global features are used for comparison. By comparing the ranks for global and local features, the false acceptance rate for DCT can be minimized. Keywords- face recognition; biometrics; person identification; authentication; discrete cosine transform; DCT; global local features. I.
My dear fellow exporters, EEPC India will be making a series of presentation with the Ministry of... more My dear fellow exporters, EEPC India will be making a series of presentation with the Ministry of Commerce & Industry, Government of India. The 1st meeting will be the Transaction Cost Core Group which will be chaired by the Hon'ble Minister of State for Commerce & Industry, Shri Jyotiraditya Scindia. In this meeting, EEPC India will be representing with the
Causality knowledge is vital to building robust AI systems. Deep learning models often perform po... more Causality knowledge is vital to building robust AI systems. Deep learning models often perform poorly on tasks that require causal reasoning, which is often derived using some form of commonsense knowledge not immediately available in the input but implicitly inferred by humans. Prior work has unraveled spurious observational biases that models fall prey to in the absence of causality. While language representation models preserve contextual knowledge within learned embeddings, they do not factor in causal relationships during training. By blending causal relationships with the input features to an existing model that performs visual cognition tasks (such as scene understanding, video captioning, video questionanswering, etc.), better performance can be achieved owing to the insight causal relationships bring about. Recently, several models have been proposed that have tackled the task of mining causal data from either the visual or textual modality. However, there does not exist wi...
Recently, learning-based models have enhanced the performance of single-image super-resolution (S... more Recently, learning-based models have enhanced the performance of single-image super-resolution (SISR). However, applying SISR successively to each video frame leads to a lack of temporal coherency. Convolutional neural networks (CNNs) outperform traditional approaches in terms of image quality metrics such as peak signal to noise ratio (PSNR) and structural similarity (SSIM). On the other hand, generative adversarial networks (GANs) offer a competitive advantage by being able to mitigate the issue of a lack of finer texture details, usually seen with CNNs when super-resolving at large upscaling factors. We present iSeeBetter, a novel GAN-based spatio-temporal approach to video super-resolution (VSR) that renders temporally consistent super-resolution videos. iSeeBetter extracts spatial and temporal information from the current and neighboring frames using the concept of recurrent back-projection networks as its generator. Furthermore, to improve the “naturality” of the super-resolve...
In the domain of Biometrics, recognition systems based on iris, fingerprint or palm print scans e... more In the domain of Biometrics, recognition systems based on iris, fingerprint or palm print scans etc. are often considered more dependable due to extremely low variance in the properties of these entities with respect to time. However, over the last decade data processing capability of computers has increased manifold, which has made real-time video content analysis possible. This shows that
2010 11th International Conference on Control Automation Robotics & Vision, 2010
In this paper, an efficient method for face recognition based on the Discrete Cosine Transform (D... more In this paper, an efficient method for face recognition based on the Discrete Cosine Transform (DCT), Fisher Linear Discriminant (FLD) and classifier is presented. First, the dimensionality of the original face image is reduced using the DCT and illumination variations are alleviated by discarding the first few low-frequency DCT coefficients. FLD is applied to the selected DCT coefficients to discriminate
Recognition systems are commonly designed to authenticate users at the access control levels of a... more Recognition systems are commonly designed to authenticate users at the access control levels of a system. A number of voice recognition methods have been developed using a pitch estimation process which are very vulnerable in low Signal to Noise Ratio (SNR) environments thus, these programs fail to provide the desired level of accuracy and robustness. Also, most text independent speaker recognition programs are incapable of coping with unauthorized attempts to gain access by tampering with the samples or reference database. The proposed text-independent voice recognition system makes use of multilevel cryptography to preserve data integrity while in transit or storage. Encryption and decryption follow a transform based approach layered with pseudorandom noise addition whereas for pitch detection, a modified version of the autocorrelation pitch extraction algorithm is used. The experimental results show that the proposed algorithm can decrypt the signal under test with exponentially ...
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