Regulatory status: For research use only, not for use in diagnostic procedures.

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cultured cells
Species Reactivity
Human, Mouse
Intended Use
The FER Cell-Based ELISA Kit is a convenient, lysate-free, high throughput and sensitive assay kit that can monitor FER protein expression profile in cells. The kit can be used for measuring the relative amounts of FER in cultured cells as well as screening for the effects that various treatments, inhibitors (ie. siRNA or chemicals), or activators have on FER.
Contents of Kit
1. 96-Well Cell Culture Clear-Bottom Microplate: 1 plate
2. 10x TBS: 24 mL (10x), Clear
3. Quenching Buffer: 24 mL (1x), Clear
4. Blocking Buffer: 50 mL (1x), Clear
5. 10x Wash Buffer: 50 mL (10x), Clear
6. 100x Anti-FER Antibody (Rabbit Polyclonal): 60 μL (100x), Purple
7. 100x Anti-GAPDH Antibody (Mouse Monoclonal): 60 μL (100x), Green
8. HRP-Conjugated Anti-Rabbit IgG Antibody: 6 mL (1x), Glass
9. HRP-Conjugated Anti-Mouse IgG Antibody: 6 mL (1x), Glass
10. Primary Antibody Diluent: 12 mL (1x), Clear
11. Ready-to-Use Substrate: 12 mL (1x), Brown
12. Stop Solution: 12 mL (1x), Clear
13. Crystal Violet Solution: 6 mL (1x), Glass
14. SDS Solution: 24 mL (1x), Clear
15. Adhesive Plate Seals: 4 seals
4°C/6 Months


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Multiple Attention Network for Facial Expression Recognition


Authors: Gan, Yanling; Chen, Jingying; Yang, Zongkai; Xu, Luhui

One key challenge in facial expression recognition (FER) is the extraction of discriminative features from critical facial regions. Because of their promising ability to learn discriminative features, visual attention mechanisms are increasingly used to address pattern recognition problems. This paper presents a novel multiple attention network that simulates humans & x2019; coarse-to-fine visual attention to improve expression recognition performance. In the proposed network, a region-aware sub-net (RASnet) learns binary masks for locating expression-related critical regions with coarse-to-fine granularity levels and an expression recognition sub-net (ERSnet) with a multiple attention (MA) block learns comprehensive discriminative features. Embedded in the convolutional layers, the MA block fuses diversified attention using the learned masks from the RASnet. The MA block contains a hybrid attention branch with a series of sub-branches, where each sub-branch provides region-specific attention. To explore the complementary benefits of diversified attention, the MA block also has a weight learning branch that adaptively learns the contributions of the different critical regions. Experiments have been carried out on two publicly available databases, RAF and CK & x002B;, and the reported accuracies are 85.69 & x0025; and 96.28 & x0025;, respectively. The results indicate that our method achieves competitive or better performance than state-of-the-art methods.

Accurate computing of facial expression recognition using a hybrid feature extraction technique


Authors: Kommineni, Jenni; Mandala, Satria; Sunar, Mohd Shahrizal; Chakravarthy, Parvathaneni Midhu

Facial expression recognition (FER) serves as an essential tool for understanding human emotional behaviors. Facial expressions provide a wealth of information about intentions, emotions, and other inner states. Over the past two decades, the development of an automatic FER device has become one of the most demanding multimedia research areas in human-computer interaction systems. Several automatic systems have been introduced and have achieved precise identification accuracies. Due to the complex nature of the human face, however, problems still exist. Researchers are still struggling to develop effective methods for extracting features from images because of unclear features. This work proposes a methodology that improves high-performance computing in terms of the facial expression recognition accuracy. To achieve the goal of high accuracy, a hybrid method is proposed using the dual-tree m-band wavelet transform (DTMBWT) algorithm based on energy, entropy, and gray-level co-occurrence matrix (GLCM). It is accompanied by the use of a Gaussian mixture model (GMM) as the classification scheme to provide efficient identification of database images in terms of facial expressions. Using the DTMBWT, it is possible to derive many expression features from decomposition levels 1 to 6. Moreover, along with the GLCM features, the contrast and homogeneity features can be retrieved. All the features are eventually categorized and recognized with the aid of the GMM classifier. The proposed algorithms are tested using Japanese Female Facial Expression (JAFFE) database with seven different facial expressions: happiness, sadness, anger, fear, neutral, surprise, and disgust. The results of the experiments show that the highest precision of the proposed technique is 99.53%, which is observed at the 4(th) decomposition level of the DTMBWT.

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