Improved Iris Recognition Using Eigen Values for Feature Extraction for Off Gaze Images

Sayed, Asim; Sardeshmukh, M. M.; Limkar, Suresh

doi:10.1007/978-3-319-03095-1_20

Cited by 5 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep convolutional neural networks were used in the authors' [16] suggested method for transfer learning as it relates to the recognition of voice emotions (DCNNs). For voice recognition, they made use of pre-trained models and then fine-tuned them using their own dataset.…”

Section: Research Articlementioning

confidence: 99%

A Transfer Learning-based Approach for Multimodal Emotion Recognition

Jain¹

2020

TURCOMAT

View full text Add to dashboard Cite

The topic of multimodal emotion recognition is one that is expanding at a rapid rate. The goal of this field is to identify and comprehend human emotions through the use of many modalities, such as speech, facial expressions, and physiological data. Transfer learning strategies have been found to be successful in overcoming the issues of processing and integrating material from a variety of modalities, as demonstrated by the findings of a number of studies. For testing multimodal emotion detection models, it is helpful to make use of publicly accessible datasets like IEMOCAP, EmoReact, and AffectNet. They provide useful resources. Data variability, data quality, modality integration, limited labelled data, privacy and ethical issues, and interpretability are only few of the hurdles that must be overcome in order to construct accurate and effective models. In order to address these challenges, a multidisciplinary approach must be taken, and research must continue to be conducted in this area. The goal of this research is to develop more robust and accurate models for multimodal emotion recognition that can be applied across a variety of contexts and populations.

show abstract

Section: Research Articlementioning

confidence: 99%

A Transfer Learning-based Approach for Multimodal Emotion Recognition

Jain¹

2020

TURCOMAT

View full text Add to dashboard Cite

show abstract

“…Fusion Code is a feature vector created by coding the phases after merging the filter outputs. To compare the similarity of two Fusion Codes, use the normalised hamming distance; then, apply a dynamic threshold to make your choice [7]. According to this procedure, the main lines on the palm were used to verify the palm print.…”

Section: Research Articlementioning

confidence: 99%

Versatile Video Coding (VVC) Standard: Overview and Applications

Mishra¹

2019

TURCOMAT

View full text Add to dashboard Cite

Information security includes picture and video compression and encryption since compressed data is more secure than uncompressed imagery. Another point is that handling data of smaller sizes is simple. Therefore, efficient, secure, and simple data transport methods are created through effective data compression technology. Consequently, there are two different sorts of compression algorithm techniques: lossy compressions and lossless compressions. Any type of data format, including text, audio, video, and picture files, may leverage these technologies. In this procedure, the Least Significant Bit technique is used to encrypt each frame of the video file format to be able to increase security. The primary goals of this procedure are to safeguard the data by encrypting the frames and compressing the video file. Using PSNR to enhance process throughput would also enhance data transmission security while reducing data loss.

show abstract

“…Results of the average execution time of loading the image, segmentation, normalization and feature encoding, are presented. Sayed et al (2014) Improved Iris Recognition Using Eigen Values for Feature Extraction for Off Gaze Images, discusses various Iris recognition and identification schemes known to produce exceptional results with very less errors and at times no errors at all but are patented. Many prominent researchers have given their schemes for either recognition of an Iris from an image and then identifying it from a set of available database so as to know who it belongs to.…”

Section: Literature Reviewmentioning

confidence: 99%

Novel Segmentation of Iris Images for Biometric Authentication Using Multi Feature Volumetric Measure

Lavanya¹,

Sabeenian²

2015

RJASET

View full text Add to dashboard Cite

The aim of the research is to improve the efficiency of biometric authentication using different features of iris image. The biometric authentication and verification has become more popular where the authentication is more essential in many organizations. There are many approaches has been discussed to segment the iris image and to perform verification but suffers with the problem of accuracy in feature extraction and segmentation. To resolve such problems and to improve the efficiency of iris segmentation and recognition, we propose a novel segmentation algorithm which uses multi level filter which removes the eyelids and eyelash features and performs the edge detection to identify the inner and outer eye regions. Once the regions has been identified then, we compute various measures like the size of inner and outer eyes and extract the features of both and convert them in to feature vectors. The generated feature vectors are used to perform classification in biometric authentication approach. The multi feature volumetric measure is computed on the feature vector of each eye image where the feature vector has various features like the size of both inner and outer eyes, width and height, the original binary features, the number of binary ones and the number of pixels damaged by any form of disease and so on. Based on these features the MFVM is computed to classify the iris image towards a big data set of biometric features to perform authentication. The proposed method has improved the efficiency of iris segmentation and improved the efficiency of iris recognition based biometric authentication. Also the approach has reduced the time complexity and improved the efficiency also.

show abstract

Improved Iris Recognition Using Eigen Values for Feature Extraction for Off Gaze Images

Cited by 5 publications

References 13 publications

A Transfer Learning-based Approach for Multimodal Emotion Recognition

A Transfer Learning-based Approach for Multimodal Emotion Recognition

Versatile Video Coding (VVC) Standard: Overview and Applications

Novel Segmentation of Iris Images for Biometric Authentication Using Multi Feature Volumetric Measure

Contact Info

Product

Resources

About