Face identification in videos from mobile cameras

It is still challenging to recognize faces reliably in videos from mobile camera, although mature automatic face recognition technology for still images has been available for quite some time. Suppose we want to be alerted when suspects appear in the recording of a police Body-Cam, even a good face matcher on still images would give many false alarms due to the uncontrolled conditions. This paper presents an approach to identify faces in videos from mobile cameras. A commercial face matcher FaceVACS is used to process the face recognition frame by frame. On a video of certain length, in order to suppress the false alarms, we propose to count the recognized identities and set thresholds to the counts, as well as to the matching scores for still-image face recognition. In this way, the facial information of a single subject over time is exploited without implementing face tracking, which is complicated and more difficult for low-quality unconstrained videos. For experiments, videos are recorded by two type of mobile cameras, which provide different video qualities. The results demonstrate the efficiency of our proposed approach

Fourier spectral of PalmCode as descriptor for palmprint recognition

Study on automatic person recognition by palmprint is currently a hot topic. In this paper, we propose a novel palmprint recognition method by transforming the typical palmprint phase code feature into its Fourier frequency domain. The resulting real-valued Fourier spectral features are further processed by horizontal and vertical 2DPCA method, which proves highly efficient in terms of computational complexity, storage requirement and recognition accuracy. This paper also gives a contrast study on palm code and competitive code under the proposed feature extraction framework. Besides, experimental results on the Hongkong PolyU Palmprint database demonstrate that the proposed method outperforms many currently reported local Gabor pattern approaches for palmprint recognition

Binary palmprint representation for feature template protection

The major challenge of biometric template protection comes from the intraclass variations of biometric data. The helper data scheme aims to solve this problem by employing the Error Correction Codes (ECC). However, many reported biometric binary features from the same user reach bit error rate (BER) as high as 40%, which exceeds the error correcting capability of most ECC (less than 25%). Therefore, a novel palmprint binary feature extraction method is proposed in this paper. The real-valued features are firstly extracted. Then one-bit quantization and reliable bits selection are processed. For verification multiple samples are required to be enrolled while training is not necessary. Experiments have been carried out on the HongKong PolyU Palmprint database. Results show that our method achieves much lower BER, lower verification error rate and allows a secret key long enough for security

Binary gabor statistical features for palmprint template protection

The biometric template protection system requires a highquality biometric channel and a well-designed error correction code (ECC). Due to the intra-class variations of biometric data, an efficient fixed-length binary feature extractor is required to provide a high-quality biometric channel so that the system is robust and accurate, and to allow a secret key to be combined for security. In this paper we present a binary palmprint feature extraction method to achieve a robust biometric channel for template protection system. The real-valued texture statistical features are firstly extracted based on Gabor magnitude and phase responses. Then a bits quantization and selection algorithm is introduced. Experimental results on the HongKong PloyU Palmprint database verify the efficiency of our method which achieves low verification error rate by a robust palmprint binary representation of low bit error rate

Global Analysis of Gene Expression Profiles in Developing Physic Nut (Jatropha curcas L.) Seeds

Background: Physic nut (Jatropha curcas L.) is an oilseed plant species with high potential utility as a biofuel. Furthermore, following recent sequencing of its genome and the availability of expressed sequence tag (EST) libraries, it is a valuable model plant for studying carbon assimilation in endosperms of oilseed plants. There have been several transcriptomic analyses of developing physic nut seeds using ESTs, but they have provided limited information on the accumulation of stored resources in the seeds. Methodology/Principal Findings: We applied next-generation Illumina sequencing technology to analyze global gen

Texture representation for low-resolution palmprint recognition

Person recognition plays an important role in our society and world. This can be observed in varieties of application scenarios such as access control, data management, national ID and forensics. The typical approaches for linking an individual to his/her identity are based on the personal possessions or knowledge, which have the disadvantages of constantly being forgotten, lost or stolen. In the last decades, person identification based on “who you are” has been intensively developed. This is commonly referred to as Biometrics. In this field, the link between an individual and his/her identity is automatically and uniquely established by a human’s intrinsic physiological or behavioral trait, such as face, iris, fingerprint, palmprint, finger vein pattern, voice, signature, gait and so on. Our research is about online person recognition based on the low-resolution palmprint images. The question is how to construct the discriminative palmprint representation for high recognition performance. It is a challenge due to the intra-class variations and inter-class similarities. Furthermore, the widespread use of biometric systems creates security and privacy risks, which have been concerned with increasing attention recently. To mitigate those risks, template-protection technology has been developed as a solution to safeguarding the stored biometric templates. For its successful implementation, the biometrical representation is generally required to be quantized into bits, which are expected to be as discriminative and reliable as possible. This is challenging since the biometric data is highly noisy. Therefore, how to construct reliable binary palmprint representations is investigated in our research work. The major palmprint characteristics are lines and wrinkles. In this thesis, these low-resolution palmprint images are treated as texture images. Accordingly, texture analysis technologies are mainly investigated for palmprint representation. The involved strategies mainly include the multi-scale and multi-orientational transform, region or pixel based statistical features extraction, feature reduction, feature quantization and bits-selection

Bits extraction for palmprint template protection with Gabor magnitude and multi-bit quantization

In this paper, we propose a method of fixed-length binary string extraction (denoted by LogGM_DROBA) from low-resolution palmprint image for developing palmprint template protection technology. In order to extract reliable (stable and discriminative) bits, multi-bit equal-probability-interval quantization and detection rate optimized bit allocation (DROBA) are operated on the real-valued features, which are resulted from representing the palmprint image by simple statistics on logarithmic transform of Gabor magnitude (LogGM). Assuming the Helper Data Scheme with a BCH error correction coding is adopted for template protection, the performance is evaluated on the Hong Kong PolyU palmprint database. The experimental results show that our method can achieve low Bit Error Rate (BER) resulted from genuine binary strings so that a long secret key (around 100 bits) is allowed to be combined for security, and low False Rejection Rate and low False Acceptance Rate (FRR/FAR) when the key retrial process is considered as a Hamming distance classifier, which verify the high stability and strong distinctive ability of our extracted palmprint binary string