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Our aim is to use randomly generated image transformation in order to obtain image features of low dimensionality. The transformation consists of local projections of spatiallyorganized parts of an image, for example rectangular image blocks. After this transformation the content of an image is hidden and will not be stably recoverable, so it can be used in systems where privacy-preserving property...
Automated human facial image de-identification is a much needed technology for privacy-preserving social media and intelligent surveillance applications. Other than the usual face blurring techniques, in this work, we propose to achieve facial anonymity by slightly modifying existing facial images into "averaged faces" so that the corresponding identities are difficult to uncover. This approach...
Preserving the privacy of biometric data becomes a critical work. To increase the privacy of the biometric data, novel method is proposed. In this proposed method, two different biometric data such as features from fingerprint and face are combined. In the face, the features like eyes, lips and brow are extracted. In the fingerprint, orientation feature is extracted. The database contains the training...
This paper presents a privacy preserved face recognition framework which combines cryptographic techniques and image processing operations. Unlike other approaches, in this work, face recognition is directly applied on the encrypted data. Cryptographic keys are generated by using a random number generator based on a continuous chaotic system, which is proved to be unpredictable. One key is generated...
An experimental tattoo de-identification system for privacy protection in still images is described in the paper. The system consists of the following modules: skin detection, region of interest detection, feature extraction, tattoo database, matching, tattoo detection, skin swapping, and quality evaluation. Two methods for tattoo localization are presented. The first is a simple ad-hoc method based...
In recent years, it is necessary to protect individual privacy from unauthenticated persons. This paper presents a biométrie encryption using face recognition system to identify known and unsuspected persons in watch list applications. The face recognition system is done by the Eigen face approach by PCA algorithm to simplify the authentication process, then to secure the Eigen faces generate the...
Positive emotions have been proven to be a key factor for successful learning. In modern personalized learning environments informal learning takes a prominent role and with this the use of computer-mediated communication. Communication data, like for example chat logs, can be harvested for sentiments. Most sentiment analyses operate processing only verbal information. But the messages exchanged in...
The binary quantization method which is used in random projection based biometric hashing systems reduces the authentication performance of these systems. In this paper, we propose new statistical quantization methods for biometric hashing systems. Our proposed quantization methods use Gaussian mixture model and Gaussian distributions to determine quantization threshold value. Therefore, we improve...
In this paper, we propose a novel face image hashing method based on an optimal linear transformation. In the proposed method, first, we apply a feature extraction method. Then, we define an optimal linear transformation matrix based on within-class covariance matrix which is the maximum likelihood estimate of the variations of the biometric data belonging to the same user. Next, we reduce the dimension...
Preserving the privacy of digital biometric data (e.g., face images) stored in a central database has become of paramount importance. This work explores the possibility of using visual cryptography for imparting privacy to biometric data such as fingerprint images, iris codes, and face images. In the case of faces, a private face image is dithered into two host face images (known as sheets) that are...
We propose in this paper a low cost multimodal biometric system combining keystroke dynamics and 2D face recognition. The objective of the proposed system is to be used while keeping in mind: good performances, acceptability, and espect of privacy. Different fusion methods have been used (min, max, mul, svm, weighted sum configured with genetic algorithms, and, genetic programming) on the scores of...
This paper presents the design and development of a technique referred to as SIPPA - Secure Information Processing with Privacy Assurance - for biometric data reconstruction. SIPPA enables a client/server model with the following two properties: (1) the client party can compare the similarity between his/her sample data with the source data on the server side - without each party revealing his/her...
In this paper, we propose a secure and robust biometric hashing method. We use Radon transform, min-max quantizer and Reed-Muller decoder in this method. Our goal is to preserve privacy in a biometric recognition system while achieving the desirable accuracy rate. We do the verification using the hash values and hence increase the security of the system. In addition, we guarantee the irreversibility...
The statistical analysis of data stored in data warehouses is an important phase in the organisation's strategic planning process. For the maximum benefit to be gained from such data warehouses, a relationship of trust needs to exist between all parties involved. In this paper we investigate its importance with respect to the statistical security problem. Understanding trust relationships in this...
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