Multimodal biometrics technology has recently gained interest due to its capacity to overcome certain inherent limitations of the single biometric modalities and to improve the overall recognition rate. A common biometric recognition system consists of sensing, feature extraction, and matching modules. The robustness of the system depends much more on the reliability to extract relevant information from the single biometric traits. This paper proposes a new feature extraction technique for a multimodal biometric system using face–iris traits. The iris feature extraction is carried out using an efficient multi-resolution 2D Log-Gabor filter to capture textural information in different scales and orientations. On the other hand, the facial features are computed using the powerful method of singular spectrum analysis (SSA) in conjunction with the wavelet transform. SSA aims at expanding signals or images into interpretable and physically meaningful components. In this study, SSA is applied and combined with the normal inverse Gaussian (NIG) statistical features derived from wavelet transform. The fusion process of relevant features from the two modalities are combined at a hybrid fusion level. The evaluation process is performed on a chimeric database and consists of Olivetti research laboratory (ORL) and face recognition technology (FERET) for face and Chinese academy of science institute of automation (CASIA) v3.0 iris image database (CASIA V3) interval for iris. Experimental results show the robustness.

Belguidoum A, Haichour R, Lograda T, Ramdani M. 2021. Biomonitoring of air pollution by lichen diversity in the urban area of Setif, Algeria. Biodiversitas 23: 970-981. The use of living organisms in air quality monitoring has received increasing attention in recent years. Lichens, pioneer and colonizing organisms, are directly sensitive to environmental changes. This results in a loss of vitality or complete destruction of the thallus. The objective of this study is to assess and map the air quality of the Setif region, Algeria by lichens, using bio-indication indices. For air quality assessment, lichen species frequency, the Shannon-Wiener index, Atmospheric Purity Index (IAP), and Air Quality Index (IAQ) were used. Sixty stations were sampled across the region, representing the various level of landscape factors and anthropogenic activities. The study recorded 54 lichen species in urban areas of Setif belonged to 29 genera and 19 families, of which crustose and foliose, were the most common in the region. The pollution indicators (IAP, IAQ) showed an important correlation between them and a difference between rural and urban ecosystems. Anthropized urban areas showed very high air pollution. This is probably due to industrial and agricultural activities, but especially to gas emissions from vehicles. The majority of studied stations belonged to an area with very high atmospheric pollution. In addition, the presence of the species Xanthoria parietina in all the stations studied facilitates to use of it as a reliable biomonitor of plant tolerance to pollutants in urban ecosystems.

Neural network classifying method is used in this work to perform facial expression recognition. The processed expressions were the six most pertinent facial expressions and the neutral one. This operation was implemented in three steps. First, a neural network, trained using Zernike moments, was applied to the set of the well known Yale and JAFFE database images to perform face detection. In the second step, detected faces are processed to perform the characterization phase through computed vectors of Zernike moments. At last step, a back propagation neural network was trained to distinguish between the seven emotion's states of a presented face. Finally, method performances were evaluated on the well known JAFEE and YALE database.