3D Face Recognition using Significant Point based SULD Descriptor

In this work, we present a new 3D face recognition method based on Speeded-Up Local Descriptor (SULD) of significant points extracted from the range images of faces. The proposed model consists of a method for extracting distinctive invariant features from range images of faces that can be used to perform reliable matching between different poses of range images of faces. For a given 3D face scan, range images are computed and the potential interest points are identified by searching at all scales. Based on the stability of the interest point, significant points are extracted. For each significant point we compute the SULD descriptor which consists of vector made of values from the convolved Haar wavelet responses located on concentric circles centred on the significant point, and where the amount of Gaussian smoothing is proportional to the radii of the circles. Experimental results show that the newly proposed method provides higher recognition rate compared to other existing contemporary models developed for 3D face recognition

Cocoa Care - An Android Application for Cocoa Disease Identification

India is an agricultural country. The correct and timely identification of diseases in crops is very much essential in agriculture. To obtain more valuable products, a product quality control is basically mandatory. Cocoa is an economically important crop that nowadays enlarges its production in southern India. To assist the farmers growing cocoa, we developed an android application Cocoa-Care. This application automatically identifies the diseases of cocoa crops, thereby helps the farmers who have little or no information about the disease. This application is developed by applying digital image processing techniques on the diseased cocoa images. Our approach replaces the manual disease inspection by the android application that identifies the cocoa disease from the captured image and suggests the possible remedies for the farmer. We used moment based texture features for the image representation and description. The matching is performed by nearest neighbor classifier. The results obtained are promising and this application can be used in the real time