Farrukh Sayeed

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Facial emotion recognition is the most significant parameter for an efficacious Human Machine Interaction (HMI). It plays a crucial role in interpreting and communicating with the people who have speaking impairments as well as a medium to understand and communicate with infants who cannot emote their feelings verbally. In this paper, we propose a hybrid(More)
This paper presents the properties of information sets that help derive local features from a face when partitioned into windows and devises the information rules from the generalized fuzzy rules for information processing that helps match the unknown test face with the known for authenticating a user. information set is constituted from the information(More)
As a first study, the use the Gabor filter bank is made to generate features for face recognition. The features so obtained on the application of SVM classifier yields accuracy rate of 96.2%. With a view to improve the performance, two more feature types, viz., wavelet features and wavelet-fuzzy features resulting from the application of 2D wavelet(More)
A novel segmentation method is developed for segmenting an Iris image into 6 circular segments after it has been cropped from an eye. Each segment is treated as a separate image to find Eigeniris and to recognize the Iris by the segmental Euclidean distance between the features of training and test samples computed from their segments. In addition to the(More)
An attempt has been made in this paper to derive the features using the Bandelet and Shearlet Transforms. The transforms are then modified into fuzzy Bandelet and fuzzy shearlet transforms and the same are then applied on the face AT&T database to extract the fuzzy features. These features are then tested with the standard Support Vector Machine(More)
Facial expression recognition is the most important criteria for effective Human Computer Interaction (HCI) as well as a medium to understand and communicate with children who cannot emote verbally. In this paper, we propose a feature extraction technique by embedding 2D-LDA and 2D-PCA. The features extracted were then tested on standard classifiers i.e.,(More)
This paper presents a work about palm print recognition using fuzzy entropy. A number of schemes have been proposed to combine the fuzzy set theory and its application to the entropy concept for modelling a palm print recognition system. The measure of uncertainty is adopted as a measure of information. Hence, the measures of fuzziness are known as fuzzy(More)
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